Compositions of beta-glucuronidase enzyme blends with enhanced enzymatic activity and methods of preparation thereof

ABSTRACT

Blends of enzymes of the same enzyme class (same EC number), such as beta-glucuronidase enzyme blends, are provided that exhibit synergistic levels of activity across a range of substrates as compared to the activity levels of each enzyme in the blend individually. The blends also may exhibit a greater effective substrate range, as well as greater effective pH and/or temperature ranges as compared to single enzyme preparations. Predictive methods for determining optimal enzyme blends, methods for preparing enzyme blends, enzyme blend compositions, enzyme blend formulations and methods of using such enzyme blends are provided.

RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No. 16/528,292 (Allowed), filed Jul. 31, 2019, which claims priority to U.S. Provisional Application No. 62/713,188, filed Aug. 1, 2018, and U.S. Provisional Application No. 62/754,358, filed Nov. 1, 2018. The entire contents of the aforementioned applications are incorporated herein by reference.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Dec. 10, 2021, is named IMJ-009DV_Sequence_Listing.txt and is 635,664 bytes in size.

BACKGROUND OF THE INVENTION

In mammals, glucuronidation via the UDP glucuronyl transferase system is one of the principle means of detoxifying or inactivating compounds. Compounds are conjugated by the glucuronyl transferase system to form glucuronides, which are then secreted in urine or into the lower intestine in bile. The beta-glucuronidase (BGUS) enzyme catalyzes the hydrolysis of a wide variety of beta-glucuronides. Given the key role of glucuronidation in detoxification of compounds, the BGUS enzyme has been used for detection of drugs in bodily samples, such as to detect the presence of therapeutic drugs in bodily samples of hospital patients. For example, a bodily sample can be tested for the presence of a drug by treating the sample with BGUS and detecting the hydrolysis product of the glucuronide form of the drug. The hydrolysis of the glucuronide by the BGUS enzyme facilitates the analysis of the drug by methods such as mass spectrometry, since this analytical instrument is less sensitive to the glucuronide.

Beta-glucuronidases (BGUS; EC 3.2.1.31) hydrolyze the beta-glycosidic bond between the anomeric reducing end of glucuronic acid (GlcU or gluc) and a broad range of possible aglycones. Enzymes with this activity are found predominantly in glycosyl hydrolase family 2 (GH2), although other examples can also be found in GH30 (Sakurama et al. (2014), Appl. Microbiol. Biotechnol. 98: 4021), GH1, GH79 and GH137. All BGUS enzymes characterized thus far are retaining enzymes, utilizing a double displacement reaction with a covalent enzyme-glucuronic acid intermediate to add water across the glycosidic bond (Wang and Trouser (1972) J. Biol. Chem. 247:2644; Davies and Henrissat (1995) Structure 3:853). Well characterized examples come from bacteria, particularly E. coli gusA (formerly uidA), mammalian species (bovine, mouse, rat, human), where the enzyme normally localizes to lysosomes and defects may contribute to lysosomal storage disorders, and mollusks (snail, abalone, limpet), the source of crude hydrolytic extracts utilized for both research and applications in forensic and clinical medicine (Graef et al. (1977) Clin. Chem. 23:532; Romberg and Lee (1995) J. Anal. Toxicol. 19:157; Yang et al. (2016) J. Anal. Toxicol. 40:323). Additional applications for the preparation of food additives and traditional remedies are under consideration (Kim et al. (2009) J Microbiol. Biotechnol. 19:1650; Sakurama et al. (2014) Appl. Microbiol. Biotechnol. 98:4021).

Genes for BGUS, especially gusA, have been favored as reporters in gene regulation studies because of the wide range of substrates with easily detected aglycone products (non-limiting example of aglycones include p-nitrophenol, phenolphthalein, 4-methylumbelliferone, indigo-blue, fluorescein). This is particularly true for studies in plants, where for many years BGUS activity was believed to be absent. Evidence of naturally occurring BGUS in plants has been discovered, but plant enzymes generally have lower pH optima and growth-specific expression patterns (Sudan et al. (2006) Planta 224:853).

Structures for GH2 BGUS enzymes have been solved from multiple sources, both with and without ligands in the active site (Jain et al. (1996) Nature Struct. Biol. 3:375; Wallace et al. (2010) Science 330:831; Roberts et al. (2013) Mol. Pharmacol. 84:208; Hassan et al. (2013) PLoS ONE 8:e79687; Wallace et al. (2015) Chem. Biol. 22:1238; Pollet et al. (2017) Structure 25:967). The approximately 70 kD enzyme monomer (approximately 600 amino acids) has three domains: an N-terminal beta-sandwich described as a sugar-binding domain (approximately 180 amino acids); a second beta-sandwich described as an immunoglobulin-like domain (approximately 110 amino acids); and an alpha/beta eight-strand TIM barrel (approximately 310 amino acids) containing the active site. The monomers form dimers with the individual active sites directly opposite each other. The dimers form tetramers, which are thought to be the active form (Stahl and Touster (1971) J. Biol. Chem. 246:5398; see also Yeom et al. (2017) PLoS ONE 12:e0170398). Additionally, higher order complexes have been observed, although their physiological relevance, if any, is unknown.

As discussed above, one significant application for BGUS enzymes is in clinical and forensic analysis of biological samples for the quantitative measurement of drugs and metabolites. In the body, toxic metabolites and foreign molecules such as drugs are glucuronidated by enzymes in the liver to increase their solubility and tag them for excretion. Thus, a broad range of glucuronidated molecules end up in the urine and other bodily fluids. To identify and quantify these molecules (“target substrates”), the preferred approach is to remove excretion tags such as glucuronic acids (some molecules are sulfated as well) and quantify the free aglycones by separation methods such as liquid chromatography (LC) and gas chromatography (GC), and methods for detection, identification and quantitation such as mass spectrometry (MS). Protocols for de-glucuronidation of excretion products initially favored acid hydrolysis (Romberg and Lee (1995) J. Anal. Toxicol. 19:157; Wang et al. (2006) J. Anal. Toxicol. 30:570). However, though simple and broadly applicable, acid hydrolysis is slow, messy and harsh, suffering from side reactions that break down some molecules targeted for analysis (Romberg and Lee (1995) J. Anal. Toxicol. 19:157; Sitasuwan et al. (2016) J. Anal. Toxicol. 40:601). In contrast, enzymatic hydrolysis is specific, potentially fast and can be accomplished under gentle conditions (Rana et al. (2008) J. Anal. Toxicol. 32:355; Sanches et al. (2014) J. Anal. Toxicol. 36:162; Morris et al. (2014) 1 Anal. Toxicol. 38:610; Yang et al. (2016) J. Anal. Toxicol. 40:323; Cummings et al. (2018) J. Anal. Toxicol. 42:214). Thus, enzymatic methods have largely superseded acid hydrolysis.

Although the specificity of BGUS is determined by its ability to recognize glucuronic acid and the anomeric bond (Pollet et al. (2017) Structure 25:967), interactions between an aglycone and an enzyme are unique to the aglycone and enzyme pair in question, likely due to steric interactions that are not yet well characterized (Masuo et al. (2010) Drug Metab. Disp. 38:1828; Kotronoulas et al. (2017) J. Steroid Biochem. Mol. Biol. 167B:212). Crude enzyme preparations of BGUS from mollusks (e.g., snail, abalone, limpet) are commercially available and thus have been used for clinical and forensic analysis purposes. In some instances, however, crude enzyme preparations contain contaminants that interfere with either enzyme activity or downstream measurement of products (Nakamura et al. (2011) Biosci. Biotechnol. Biochem. 75:1506).

More recently, purified recombinant BGUS enzymes have been described, including variant forms of recombinant BGUS enzymes that have been modified to enhance enzymatic activity, temperature stability or both (see e.g., US Patent Publication 20160090582, issued as U.S. Pat. No. 9,920,306; US Patent Publication 20160237415, issued as U.S. Pat. No. 9,719,075; US Patent Publication 20170267985, issued as U.S. Pat. No. 9,909,111; Xiong, A-S. et al. (2007) Prot. Eng. Design Select. 20:319-325). For example, a genetically modified recombinant E. coli K12 BGUS enzyme is commercially available (IMCSzyme®; IMCS).

While crude enzyme BGUS preparations and recombinant BGUS enzymes are available and suitable for clinical and forensic applications, additional BGUS compositions, formulations and methods of making same are still needed in the art.

SUMMARY OF THE INVENTION

Because of the unique sequence and structure of each BGUS, each enzyme has a profile of activities across a range of target substrates. No homogenous single enzyme has high activity against all substrates of interest. Additionally, each enzyme's activity profile varies with respect to pH and temperature. When the enzyme is used in urine, urine specimens are highly heterogeneous and have pH ranged from 4.0 to 9.0. As a result, it is difficult to find one BGUS enzyme that serves all analytical needs under all conditions. The present disclosure addresses this unmet need by utilizing blends of highly purified preparations of BGUS enzymes from different sources to complement activities. The enzymes chosen for inclusion in an enzyme blend are chosen to cover as many different target substrates as possible and to overlap with respect to temperature and pH profiles, such that the level of activity across a range of substrates and/or across a pH range and/or across a temperature range of the enzyme blend is greater than the ranges for each enzyme alone. In particular, the enzyme blends of the disclosure have been shown experimentally to exhibit synergistic activity against a panel of substrates, not merely an additive effect (i.e., the level of activity of the blend against a panel of substrates is greater than would be theoretically expected from adding the activities of each enzyme against each substrate). Thus, the resultant enzyme blend of the disclosure is an enzyme composition of much higher utility than a single enzyme preparation.

Moreover, while initially described in the context of BGUS enzyme blends, the enzyme blending approach of the disclosure can be applied to other classes enzymes as well (i.e., to two or more enzymes having the same Enzyme Commission number), to thereby increase substrate range and activity (e.g., pH and/or temperature range conditions), as described further herein.

Accordingly, in various aspects, the disclosure pertains to predictive methods for determining optimal enzyme blends for the substrate composition of a particular intended use, methods for preparing enzyme blends, enzyme blend compositions, enzyme blend formulations and methods of using such enzyme blends.

In one aspect, the disclosure pertains to a composition comprising a blend of two or more enzymes, wherein each enzyme in the blend has the same Enzyme Commission (EC) number and wherein the level of activity of the blend for a range of substrates catalyzed by the blend is synergistically greater than the level of activity for the range of substrates catalyzed by each enzyme in the blend individually. Furthermore, in one embodiment, the effective range of substrates catalyzed by the blend is greater than the effective range of substrates catalyzed by each enzyme in the blend individually. Furthermore, in one embodiment, the effective pH range of the blend for one or more substrates is greater than the effective pH range of each enzyme in the blend individually. Furthermore, in one embodiment, the effective temperature range of the blend for one or more substrates is greater than the effective temperature range of each enzyme in the blend individually. In one embodiment, each enzyme in the blend is a beta-glucuronidase enzyme (EC number 3.2.1.31). In another embodiment, each enzyme in the blend is a sulfatase (EC number 3.1.6.1).

In another aspect, the disclosure pertains to a composition comprising a blend of two or more beta-glucuronidase enzymes (Enzyme Commission number 3.2.1.31), wherein:

-   -   (i) the level of activity of the blend for a range of substrates         catalyzed by the blend is synergistically greater than the level         of activity for the range of substrates catalyzed by each enzyme         in the blend individually;     -   (ii) the effective range of substrates catalyzed by the blend is         greater than the effective range of substrates catalyzed by each         enzyme in the blend individually; or     -   (iii) the effective pH range of the blend for one or more         substrates is greater than the effective pH range of each enzyme         in the blend individually; or     -   (iv) the effective temperature range of the blend for one or         more substrates is greater than the effective temperature range         of each enzyme in the blend individually; or     -   (v) any combination of (i)-(iv).

For enzyme blend compositions of the disclosure, each enzyme in the blend typically is purified to at least 90% homogeneity. In certain embodiments, at least one enzyme in the blend is a recombinant enzyme. In certain embodiments, the blend comprises at least two, at least three, at least four or at least five enzymes.

In certain embodiments, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises up to about 10% of the total protein mass and the second enzyme comprises up to about 90% of the total enzyme mass, or the first enzyme comprises up to about 20% of the total protein mass and the second enzyme comprises up to about 80% of the total enzyme mass, or the first enzyme comprises up to about 30% of the total protein mass and the second enzyme comprises up to about 70% of the total enzyme mass, or the first enzyme comprises up to about 40% of the total protein mass and the second enzyme comprises up to about 60% of the total enzyme mass, or the first enzyme comprises up to about 50% of the total protein mass and the second enzyme comprises up to about 50% of the total enzyme mass.

For BGUS enzyme blends, in one embodiment, the range of substrates catalyzed by the blend comprises glucuronidated metabolites of drugs comprising opiates, synthetic opioids, anti-depressants and benzodiazepines. In another embodiment, the range of substrates catalyzed by the blend comprises glucuronidated opiates comprising morphine-3-β-D-glucuronide, hydromorphone-3-β-D-glucuronide, oxymorphone-3-β-D-glucuronide, codeine-6-β-D-glucuronide and dihydrocodeine-6-β-D-glucuronide. In another embodiment, the range of substrates catalyzed by the blend comprises glucuronidated opioids comprising buprenorphine glucuronide, norbuprenorphine glucuronide and tapentadol glucuronide. In another embodiment, the range of substrates catalyzed by the blend comprises glucuronidated anti-depressants comprising O-desmethylvenlafaxine glucuronide and amitriptyline-N-β-D-glucuronide. In another embodiment, the range of substrates catalyzed by the blend comprises glucuronidated benzodiazepines comprising temazepam glucuronide, oxazepam glucuronide and lorazepam glucuronide.

For BGUS enzyme blends, in one embodiment, at least one enzyme in the blend comprises an amino acid sequence at least 90% identical to one of the amino acid sequences shown in SEQ ID NOs: 1-16. In another embodiment, at least one enzyme in the blend comprises an amino acid sequence at least 98% identical to one of the amino acid sequences shown in SEQ ID NOs: 1-16. In another embodiment, at least one of the enzymes in the blend comprises the amino acid sequence shown in SEQ ID NO: 9. In another embodiment, at least one enzyme in the blend is purified from a mollusk source, such as Helix, Haliotis, Cornu or Patella.

In another aspect, the disclosure pertains to a composition comprising a blend of Brachyspira pilosicoli beta-glucuronidase (BpGUS) and Eubacterium eligens beta-glucuronidase (EeGUS), wherein the level of activity of the blend for a range of substrates catalyzed by the blend is synergistically greater than the level of activity for the range of substrates catalyzed by each enzyme in the blend individually. In various embodiments, the blend comprises about 10%-30% BpGUS and about 70%-90% EeGUS, or about 15%-25% BpGUS and about 75%-85% EeGUS, or 15% BpGUS and 85% EeGUS or 25% BpGUS and 75% EeGUS. In one embodiment, the BpGUS comprises the amino acid sequence shown in SEQ ID NO: 5 (or an amino acid sequence 90%, 95%, 96%, 97%, 98% or 99% identical thereto). In one embodiment, the EeGUS comprises the amino acid sequence shown in SEQ ID NO: 10 (or an amino acid sequence 90%, 95%, 96%, 97%, 98% or 99% identical thereto). In one embodiment, the range of substrates against which the BpGUS/EeGUS blend exhibits synergistic activity comprises two or more substrates selected from morphine-3-β-D-glucuronide (M3G), oxymorphone-3-β-D-glucuronide (OM3G), hydromorphone-3-β-D-glucuronide (HM3G), codeine-6-β-D-glucuronide (C6G), dihydrocodeine-6-β-D-glucuronide (DHC6G), buprenorphine-3-β-D-glucuronide (BUP gluc), norbuprenorphine-3-β-D-glucuronide (NBUP gluc), tapentadol glucuronide (TAP gluc), O-desmethyltramadol glucuronide (ODT gluc), O-desmethylvenlafaxine glucuronide (ODV gluc), amitriptyline-N-β-D-glucuronide (AMT gluc), oxazepem glucuronide (OXZ gluc), lorazepam glucuronide (LOR gluc) and temazepam glucuronide (TEM gluc).

In another aspect, the disclosure pertains to a composition comprising a blend of Eubacterium eligens beta-glucuronidase (EeGUS) and a chimeric beta-glucuronidase enzyme, Rxn3, comprising the amino acid sequence shown in SEQ ID NO: 19, wherein the level of activity of the blend for a range of substrates catalyzed by the blend is synergistically greater than the level of activity for the range of substrates catalyzed by each enzyme in the blend individually. In various embodiments, the blend comprises about 70%-90% EeGUS and about 10%-30% Rxn3, or about 70%-80% EeGUS and about 20%-30% Rxn3 or 70% EeGUS and 30% Rxn3 or 80% EeGUS and 20% Rxn3. In one embodiment, the EeGUS comprises the amino acid sequence shown in SEQ ID NO: 10 (or an amino acid sequence 90%, 95%, 96%, 97%, 98% or 99% identical thereto). In one embodiment, the range of substrates against which the EeGUS/Rxn3 blend exhibits synergistic activity comprises two or more substrates selected from morphine-3-β-D-glucuronide (M3G), oxymorphone-3-β-D-glucuronide (OM3G), hydromorphone-3-β-D-glucuronide (HM3G), codeine-6-β-D-glucuronide (C6G), dihydrocodeine-6-β-D-glucuronide (DHC6G), buprenorphine-3-β-D-glucuronide (BUP gluc), norbuprenorphine-3-β-D-glucuronide (NBUP gluc), tapentadol glucuronide (TAP gluc), O-desmethyltramadol glucuronide (ODT gluc), O-desmethylvenlafaxine glucuronide (ODV gluc), amitriptyline-N-β-D-glucuronide (AMT gluc), oxazepem glucuronide (OXZ gluc), lorazepam glucuronide (LOR gluc) and temazepam glucuronide (TEM gluc).

In another aspect, the disclosure pertains to a formulation comprising an enzyme blend composition of the disclosure. In one embodiment, the formulation comprises the enzyme blend and at least one excipient, wherein each enzyme in the blend is present at a concentration of at least 0.1 mg/mL. In one embodiment, the formulation is an aqueous formulation. In another embodiment, the formulation is a lyophilized formulation. In another embodiment, the formulation is a protein pellet obtained by precipitation with an agent such as, but not limited to, ammonium sulfate. In various embodiments, at least one excipient is selected from the group consisting of water, salts, buffers, sugars and amino acids. In one embodiment, the formulation is free of polymers and detergents. Packaged formulations, comprising a formulation of the disclosure and a container, are also encompassed.

In another aspect, the disclosure pertains to a method of catalyzing a range of substrates, the method comprising contacting the range of substrates with an enzyme blend composition or formulation of the disclosure under conditions such that catalysis of the range of substrates occurs. In one embodiment, the enzyme blend is a BGUS blend and the method is a method of hydrolyzing a range of substrates comprising a glucuronide linkage, the method comprising contacting the range of substrates with a BGUS enzyme blend composition or formulation of the disclosure under conditions such that hydrolysis of the glucuronide linkage in the range of substrates occurs. In one embodiment, the range of substrates comprises opiate glucuronides. In another embodiment, the range of substrates comprises benzodiazepine glucuronides. In another embodiment, the range of substrates is in a sample of blood, urine, tissue or meconium obtained from a subject.

In yet another aspect, the disclosure pertains to a method of preparing an enzyme blend that is optimized for a given temperature, pH, and substrate target range, wherein each enzyme within the blend has the same EC number and wherein the specific activity of each enzyme on each substrate for the given temperature and pH is known, the method comprising:

-   -   (i) calculating the fractional activity of each enzyme based on         the fraction of enzyme in the blend multiplied by the specific         activity of the enzyme under the given conditions of temperature         and pH;     -   (ii) summing the fractional activities for all enzymes for each         substrate;     -   (iii) multiplying the total activities at the given temperature         and pH for each substrate in the target range;     -   (iv) maximizing the grand total activity at the given         temperature and pH by adjusting the fractions of enzyme in the         blend, to thereby obtain an optimized fraction for each enzyme         in the blend; and     -   (v) combining the optimized fraction of each enzyme together to         thereby prepare the enzyme blend.         In one embodiment, the enzyme blend is further optimized for a         broader temperature and pH range by summing the grand totals         from each temperature and pH condition and adjusting the enzyme         fractions to maximize the summed grand totals. In one         embodiment, the grand total activities are weighted in         proportion to the probability that samples to undergo enzymatic         hydrolysis will represent a particular temperature and pH         condition. In one embodiment, each enzyme in the blend is a         beta-glucuronidase enzyme (EC number 3.2.1.31). In another         embodiment, each enzyme in the blend is a sulfatase (EC number         3.1.6.1). In another embodiment, the disclosure provides a         method of preparing an enzyme blend, the method comprising         combining at least two enzymes together, wherein the fraction of         each enzyme in the blend has been determined according to the         method of the disclosure described above.

Other features and aspects of the disclosure are described in further detail herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1D show an alignment of the amino acid sequences for the EeGUS (SEQ ID NO: 10), AoGUS (SEQ ID NO: 1), Rxn3 (SEQ ID NO: 19), AtGUS (SEQ ID NO: 2), EcElF (SEQ ID NO: 9), BpGUS (SEQ ID NO: 5), BmGUS (SEQ ID NO: 6), CpGUS (SEQ ID NO: 7), StpGUS (SEQ ID NO: 15), LbLR2D (SEQ ID NO: 12), SaGUS (SEQ ID NO: 16), HsGUS (SEQ ID NO: 11), BfGUS (SEQ ID NO: 3), PmGUS (SEQ ID NO: 14) and BuGUS (SEQ ID NO: 4) enzymes. Amino acid residue numbering is shown on the right. Key conserved residues are indicated by (.) (:) and (*) beneath the sequences. Residues that were targeted for point mutagenesis to create certain variant enzymes described herein are indicated in black.

FIG. 2 is a photograph of an SDS-PAGE gel showing purification of BpGUS (Lane 1) and EeGUS (Lane 2). Molecular weight markers in kD are shown in Lane M.

FIG. 3 is a graph showing the relative enzyme activity of recombinant EcEiF, BpGUS, EeGUS, AoLi-3 and AtLi-20 BGUS enzymes across a range of pH.

FIGS. 4A-4B are a series of graphs showing the activity surface plots for BpGUS on fourteen different glucuronidated drug substrates across the indicated pH and temperature ranges.

FIGS. 5A-5B are a series of graphs showing the activity surface plots for EeGUS on fourteen different glucuronidated drug substrates across the indicated pH and temperature ranges.

FIGS. 6A-6B are a series of graphs showing the activity surface plots for the chimeric BGUS enzyme Rxn3 on fourteen different glucuronidated drug substrates across the indicated pH and temperature ranges.

FIG. 7 is a bar graph showing the blended root sum activity (in pmol/ming mg protein) against fourteen substrates for blends of BpGUS and EeGUS blended at the indicated percentages. Dark grey bars show the theoretical expected activity of the blends and light grey bars show the actual experimental activity of the blends.

FIGS. 8A-8B are a series of graphs showing the activity surface plots for an enzyme blend of EeGUS and Rxn3 on fourteen different glucuronidated drug substrates across the indicated enzyme ratios and pH ranges.

FIG. 9 is a bar graph showing the blended root sum activity (in pmol/ming mg protein) against fourteen substrates for blends of EeGUS and Rxn3 blended at the indicated percentages. Dark grey bars show the theoretical expected activity of the blends and light grey bars show the actual experimental activity of the blends.

FIGS. 10A-10B are surface plots showing the percentage of BpGUS in BpGUS/EeGUS blends at the indicated pHs and temperatures needed to achieve maximal sum of blended activity (Model 1) (FIG. 10A) or needed to achieve the minimal enzyme amount required for 15 minute complete hydrolysis (Model 2) (FIG. 10B).

FIGS. 11A-11B are surface plots showing the percentage of Rxn3 in EeGUS/Rxn3 blends at the indicated pHs and temperatures needed to achieve maximal sum of blended activity (Model 1) (FIG. 11A) or needed to achieve the minimal enzyme amount required for 15 minute complete hydrolysis (Model 2) (FIG. 11B).

DETAILED DESCRIPTION OF THE INVENTION

A challenge facing clinical and forensic laboratories when testing for drugs and metabolites in biological samples is the fact that many of the molecules of interest are modified with glucuronic acid. The glucuronic acid can be removed with an enzyme, beta-glucuronidase, of which many examples may be found in the internationally recognized enzyme category EC 3.2.1.31. These enzymes are specific for the glucuronic acid attached to another molecule. However, beta-glucuronidases vary in activity depending upon the non-glucuronic acid part of the molecule (the aglycone), and the reason for these variances in enzyme activity towards the different substrates is largely unknown. Because clinical and forensic laboratories screen and quantify for a broad range of substrates, a single enzyme is unlikely to work efficiently across the entire drug panel of interest. Furthermore, different enzymes have different optima in terms of pH and temperature.

Accordingly, the present disclosure provides blends of enzymes and a method for optimizing that blend for maximum efficiency under defined conditions against a defined panel of substrates (e.g., glucuronidated targets). As described herein, the enzyme blends of the disclosure exhibit synergistic activity across a range of substrates that exceeds what would be expected by adding the activity of each enzyme against each substrate (i.e., the enzyme blends exhibit a synergistic effect against the substrates, not simply an additive effect).

Various aspects of the disclosure are described in further detail in the subsections below:

I. Enzyme Blends

In one aspect, the disclosure pertains to compositions comprising a blend of two or more enzymes, wherein each enzyme in the blend has the same Enzyme Commission (EC) number (i.e., all enzymes in the blend catalyze the same type of enzymatic reaction). The EC number is a numerical classification scheme for enzymes based on the chemical reactions they catalyze. As a system of enzyme nomenclature, every EC number is associated with a recommended name for the respective enzyme. An EC number specifies the enzyme-catalyzed reaction carried out by every enzyme having that same EC number. Thus, different enzymes (e.g., from different organisms) that catalyze the same reaction receive the same EC number.

Blends of different enzymes that catalyze different types of enzymatic reactions, have been described in the art (see e.g., U.S. Pat. No. 4,994,390 blending a cellulase, an alpha-amylase and a protease; U.S. Pat. No. 5,071,765, blending a beta-glucanase, an alpha-amylase and a protease). However, the instant disclosure pertains to blends of enzymes, wherein each enzyme within the blend has the same EC number and thus each enzyme in the blend catalyzes the same type of enzymatic reaction.

The enzyme blends of the disclosure have the advantage that the level of activity of the blend across a range of substrates catalyzed by the blend is greater than the additive level of activity across the range of substrates catalyzed by each enzyme in the blend individually such that the blend exhibits synergistic activity across the range of substrates. As used herein, the term “synergistic activity” with respect to an enzyme blend refers to the total activity (i.e., total level of activity) of the blend being greater than the sum of the individual activities of each enzyme in the blend against a range of substrates. For example, a blend may exhibit at least 5%, at least 10%, at least 15%, at least 20% or at least 25% greater activity against a range of substrates than the additive level of activity across the range of substrates based on each enzyme in the blend. As used herein, a “range of substrates” or “panel of substrates” refers to at least two substrates that are catalyzed by the enzyme and may include three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen or more substrates.

Accordingly, in one aspect, the invention pertains to a composition comprising a blend of two or more enzymes, wherein each enzyme in the blend has the same Enzyme Commission (EC) number and wherein the level of activity of the blend for a range of substrates catalyzed by the blend is synergistically greater than the level of activity for the range of substrates catalyzed by each enzyme in the blend individually.

Furthermore, the blend may have an increased effective substrate range under particular conditions, as compared to each enzyme in the blend individually. As used herein, an “effective substrate range under particular conditions” refers to at least 50% activity (or at least 60%, at least 70% or at least 80% activity) against each substrate in the range under the same defined reaction conditions (e.g., at a particular temperature and pH). For example, a hypothetical enzyme 1 may have an effective substrate range of A, B and C at a specified temperature and pH, and a hypothetical enzyme 2 (having the same EC number as enzyme 1) may have an effective substrate range of C, D and E at that same temperature and pH. A composition comprising a blend of enzyme 1 and enzyme 2 thus may have an effective substrate range of A, B, C, D and E at that temperature and pH. Thus, this effective substrate range of the blend (i.e., A, B, C, D and E) is greater than the effective substrate range of each of the enzymes in the blend individually (i.e., greater than A, B and C for enzyme 1 and greater than C, D and E for enzyme 2).

Accordingly, in one aspect, the invention pertains to a composition comprising a blend of two or more enzymes, wherein each enzyme in the blend has the same Enzyme Commission (EC) number and wherein the effective range of substrates catalyzed by the blend is greater than the effective range of substrates catalyzed by each enzyme in the blend individually.

Additionally, in certain embodiments, the blend may have an increased effective pH range for one or more substrates, as compared to each enzyme in the blend individually. As used herein, an “effective pH range” for an individual enzyme or enzyme blend refers to the pH conditions under which the individual enzyme or enzyme blend exhibits at least 50% activity (or at least 60%, at least 70% or at least 80% activity) against a defined substrate or panel of substrates. For example, a hypothetical enzyme 1 may have an effective pH range of 5.0-6.5, and a hypothetical enzyme 2 (having the same EC number as enzyme 1) may have an effective pH range of 6.0-7.5. A composition comprising a blend of enzyme 1 and enzyme 2 thus may have an effective pH range of 5.0-7.5. Thus, this effective pH range of the blend (i.e., 5.0-7.5) is greater than the effective pH range of each of the enzymes in the blend individually (i.e., greater than 5.0-6.5 for enzyme 1 and greater than 6.0-7.5 for enzyme 2).

Accordingly, in another aspect, the invention pertains to a composition comprising a blend of two or more enzymes, wherein each enzyme in the blend has the same Enzyme Commission (EC) number and wherein the effective pH range of the blend for one or more substrates is greater than the effective pH range of each enzyme in the blend individually.

Additionally, in certain embodiments, the blend may have an increased effective the temperature range for one or more substrates, as compared to each enzyme in the blend individually. As used herein, an “effective temperature range” for an individual enzyme or enzyme blend refers to the temperature conditions under which the individual enzyme or enzyme blend exhibits at least 50% activity (or at least 60%, at least 70% or at least 80% activity) against a defined substrate or panel of substrates. For example, a hypothetical enzyme 1 may have an effective temperature range of 25−30° C., and a hypothetical enzyme 2 (having the same EC number as enzyme 1) may have an effective temperature range of 30−35° C. A composition comprising a blend of enzyme 1 and enzyme 2 thus may have an effective temperature range of 25−35° C. Thus, this effective temperature range of the blend (i.e., 25-35° C.) is greater than the temperature range of each of the enzymes in the blend individually (i.e., greater than 25-30° C. for enzyme 1 and greater than 30-35° C. for enzyme 2).

Accordingly, in another aspect, the invention pertains to a composition comprising a blend of two or more enzymes, wherein each enzyme in the blend has the same Enzyme Commission (EC) number and wherein the effective temperature range of the blend for one or more substrates is greater than the effective temperature range of each enzyme in the blend individually.

In a preferred embodiment, each enzyme in the blend is a beta-glucuronidase (BGUS) enzyme (EC number 3.2.1.31). Accordingly, in another aspect, the disclosure pertains to a composition comprising a blend of two or more beta-glucuronidase enzymes (Enzyme Commission number 3.2.1.31), wherein:

-   -   (i) the level of activity of the blend for a range of substrates         catalyzed by the blend is synergistically greater than the level         of activity for the range of substrates catalyzed by each enzyme         in the blend individually;     -   (ii) the effective range of substrates catalyzed by the blend is         greater than the effective range of substrates catalyzed by each         enzyme in the blend individually; or     -   (iii) the effective pH range of the blend is greater than the         effective pH range of each enzyme in the blend individually; or     -   (iv) the effective temperature range of the blend is greater         than the effective temperature range of each enzyme in the blend         individually; or     -   (v) any combination of (i)-(iv).         Specific beta-glucuronidase enzymes for use in the blends,         preparation thereof and substrates therefor are described in         further detail in subsection II below.

Alternatively, in other embodiments, the enzyme blend comprises two or more enzymes from categories other than BGUS enzymes. For example, enzymes that each catalyze the same specific reaction with substrates that contain both a specific and a non-specific structural feature are suitable for blending according to the approaches described herein. In general, enzyme classes according to their EC number are divided as follows: EC1: oxidoreductases (e.g., dehydrogenases, oxidases); EC2: transferases (e.g., transaminases, kinases); EC3: hydrolases (e.g., lipases, amylases, peptidases, phosphatases); EC4: lyases (e.g., decarboxylases); EC5: isomerases (e.g., isomerase, mutase); and EC6: ligases (e.g., synthetase). In one embodiment an enzyme blend of the disclosure comprises two or more sulfatases (EC 3.1.6.1).

In various embodiments, an enzyme blend of the disclosure comprises three or more enzymes having the same EC number, four or more enzymes having the same EC number, five or more enzymes having the same EC number, six or more enzymes having the same EC number, seven or more enzymes having the same EC number, eight or more enzymes having the same EC number, nine or more enzymes having the same EC number or 10 or more enzymes having the same EC number.

In certain embodiments, each enzyme in the blend is purified to at least 90% homogeneity. In certain embodiments, at least one enzyme in the blend is a recombinant enzyme. In certain embodiments, all enzymes in the blend are recombinant enzymes. In certain embodiments, at least one enzyme in the blend is a purified, naturally-occurring enzyme (i.e., a non-recombinant enzyme). In certain embodiments, all enzymes in the blend are purified, naturally-occurring enzymes (i.e., a non-recombinant enzymes). In certain embodiments, the enzymes in the blend are a combination of recombinant enzymes and purified, naturally-occurring (i.e., non-recombinant) enzymes. Specific examples of recombinant and purified, naturally-occurring BGUS enzymes suitable for use in a BGUS enzyme blend of the disclosure are described in further detail in subsection II below.

In the enzyme blends of the disclosure, two or more enzymes are mixed together in different or equal proportions in the composition. The particular fraction to be used for each enzyme in the blend can be determined by the optimization methods described herein (see subsection V and Example 5 below), and further confirmed and/or optimized experimentally as described in Example 6 below, e.g., to select particular fractions in the blend that achieve a synergistic effect across a range of substrates.

In one embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises up to about 10% of the total protein mass and the second enzyme comprises up to about 90% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises up to about 20% of the total protein mass and the second enzyme comprises up to about 80% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises up to about 30% of the total protein mass and the second enzyme comprises up to about 70% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises up to about 40% of the total protein mass and the second enzyme comprises up to about 60% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises up to about 50% of the total protein mass and the second enzyme comprises up to about 50% of the total enzyme mass. As used herein, the term “about” or “approximately”, as applied to one or more values of interest, refers to a value that is similar to a stated reference value. In certain embodiments, the term “approximately” or “about” refers to a range of values that fall within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in either direction (greater than or less than) of the stated reference value unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value). For example, when used in the context of a % amount of enzyme in a blend, “about” may mean+/−5% of the recited value.

In other embodiments, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 1%-10% of the total protein mass and the second enzyme comprises 90%-99% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 5%-15% of the total protein mass and the second enzyme comprises up to about 85%-95% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 10%-20% of the total protein mass and the second enzyme comprises 80%-90% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 15%-25% of the total protein mass and the second enzyme comprises 75%-85% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 20%-30% of the total protein mass and the second enzyme comprises 70%-80% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 25%-35% of the total protein mass and the second enzyme comprises 65%-75% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 30%-40% of the total protein mass and the second enzyme comprises 60%-70% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 35%-45% of the total protein mass and the second enzyme comprises 55%-65% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 40%-50% of the total protein mass and the second enzyme comprises 50%-60% of the total enzyme mass. In another embodiment, the blend comprises a first enzyme and a second enzyme, wherein the first enzyme comprises 45%-55% of the total protein mass and the second enzyme comprises 45%-55% of the total enzyme mass. The ordinarily skilled artisan will appreciate that other fractions of first and second enzymes are possible and can be determined according to the methods disclosed herein.

In one embodiment, the blend of the disclosure is a blend of beta-glucuronidases from Brachyspira pilosicoli (BpGUS) and Eubacterium eligens (EeGUS). BpGUS and EeGUS blends are described in detail in Example 6 and exhibit synergistic activity across a range of substrates (as shown in FIG. 6 ). In one embodiment, the range of substrates comprises morphine-3-β-D-glucuronide (M3G), oxymorphone-3-β-D-glucuronide (OM3G), hydromorphone-3-β-D-glucuronide (HM3G), codeine-6-β-D-glucuronide (C6G), dihydrocodeine-6-β-D-glucuronide (DHC6G), buprenorphine-3-β-D-glucuronide (BUP gluc), norbuprenorphine-3-β-D-glucuronide (NBUP gluc), tapentadol glucuronide (TAP gluc), O-desmethyltramadol glucuronide (ODT gluc), O-desmethylvenlafaxine glucuronide (ODV gluc), amitriptyline-N-β-D-glucuronide (AMT gluc), oxazepem glucuronide (OXZ gluc), lorazepam glucuronide (LOR gluc) and temazepam glucuronide (TEM gluc).

In one embodiment, the blend comprises about 10%-30% BpGUS and about 70%-90% EeGUS. In another embodiment, the blend comprises about 15%-25% BpGUS and about 75%-85% EeGUS. In another embodiment, the blend comprises about 15% BpGUS and about 85% EeGUS. In another embodiment, the blend comprises about 25% BpGUS and about 75% EeGUS. In another embodiment, the blend comprises 15% BpGUS and 85% EeGUS. In another embodiment, the blend comprises 20% BpGUS and 80% EeGUS. In another embodiment, the blend comprises 25% BpGUS and 75% EeGUS.

In another embodiment, the blend comprises about 10%-30% EeGUS and about 70%-90% BpGUS. In another embodiment, the blend comprises about 15%-25% EeGUS and about 75%-85% BpGUS. In another embodiment, the blend comprises about 15% EeGUS and about 85% BpGUS. In another embodiment, the blend comprises about 25% EeGUS and about 75% BpGUS. In another embodiment, the blend comprises 15% EeGUS and 85% BpGUS. In another embodiment, the blend comprises 20% EeGUS and 80% BpGUS. In another embodiment, the blend comprises 25% EeGUS and 75% BpGUS. In another embodiment, the blend comprises 50% EeGUS and 50% BpGUS.

In other embodiments, the blend comprises about 1-20% BpGUS and about 80-99% EeGUS, or about 20-40% BpGUS and about 60-80% EeGUS, or about 40-60% BpGUS and about 40-60% EeGUS, or about 50% BpGUS and about 50% EeGUS, or about 20-40% EeGUS and about 60-80% BpGUS, or about 1-20% EeGUS and about 80-99% BpGUS.

In certain embodiments, BpGUS comprises an amino acid sequence at least 90% identical (or at least 95%, 96%, 97%, 98%, 99% or 100% identical) to SEQ ID NO: 5. In certain embodiments, BpGUS is a recombinant enzyme. In certain embodiments, BpGUS is a purified, naturally-occurring enzyme.

In certain embodiments, EeGUS comprises an amino acid sequence at least 90% identical (or at least 95%, 96%, 97%, 98%, 99% or 100% identical) to SEQ ID NO: 10. In certain embodiments, EeGUS is a recombinant enzyme. In certain embodiments, EeGUS is a purified, naturally-occurring enzyme.

In one embodiment, the blend comprises 15%-25% recombinant BpGUS comprising the amino acid sequence shown in SEQ ID NO: 5 and 75%-85% recombinant EeGUS comprising the amino acid sequence shown in SEQ ID NO: 10. In one embodiment, the blend comprises 25% recombinant BpGUS comprising the amino acid sequence shown in SEQ ID NO: 5 and 75% recombinant EeGUS comprising the amino acid sequence shown in SEQ ID NO: 10.

In one embodiment, the blend comprises about 70%-90% EeGUS and about 10%-30% Rxn3. In another embodiment, the blend comprises about 75%-85% EeGUS and about 15%-25% Rxn3. In another embodiment, the blend comprises about 80% EeGUS and about 20% Rxn3. In another embodiment, the blend comprises about 70% EeGUS and about 30% Rxn3. In another embodiment, the blend comprises 80% EeGUS and 20% Rxn3. In another embodiment, the blend comprises 75% EeGUS and 25% Rxn3. In another embodiment, the blend comprises 70% EeGUS and 30% Rxn3.

In another embodiment, the blend comprises about 70%-90% Rxn3 and about 10%-30% EeGUS. In another embodiment, the blend comprises about 75%-85% Rxn3 and about 15%-25% EeGUS. In another embodiment, the blend comprises about 80% Rxn3 and about 20% EeGUS. In another embodiment, the blend comprises about 70% Rxn3 and about 30% EeGUS. In another embodiment, the blend comprises 80% Rxn3 and 20% EeGUS. In another embodiment, the blend comprises 75% Rxn3 and 25% EeGUS. In another embodiment, the blend comprises 70% Rxn3 and 30% EeGUS. In another embodiment, the blend comprises 50% Rxn3 and 50% EeGUS.

In other embodiments, the blend comprises about 1-20% Rxn3 and about 80-99% EeGUS, or about 20-40% Rxn3 and about 60-80% EeGUS, or about 40-60% Rxn3 and about 40-60% EeGUS, or about 50% Rxn3 and about 50% EeGUS, or about 20-40% EeGUS and about 60-80% Rxn3, or about 1-20% EeGUS and about 80-99% Rxn3.

In certain embodiments, EeGUS comprises an amino acid sequence at least 90% identical (or at least 95%, 96%, 97%, 98%, 99% or 100% identical) to SEQ ID NO: 10. In certain embodiments, EeGUS is a recombinant enzyme. In certain embodiments, EeGUS is a purified, naturally-occurring enzyme.

In certain embodiments, Rxn3 comprises an amino acid sequence at least 90% identical (or at least 95%, 96%, 97%, 98%, 99% or 100% identical) to SEQ ID NO: 19.

In one embodiment, the blend comprises 70%-90% recombinant EeGUS comprising the amino acid sequence shown in SEQ ID NO: 10 and 10%-30% recombinant Rxn3 comprising the amino acid sequence shown in SEQ ID NO: 19. In one embodiment, the blend comprises 70% recombinant EeGUS comprising the amino acid sequence shown in SEQ ID NO: 10 and 30% recombinant Rxn3 comprising the amino acid sequence shown in SEQ ID NO: 19.

II. Beta-Glucuronidase Enzymes

In one embodiment, an enzyme blend of the disclosure comprises two or more beta-glucuronidase enzymes (EC 3.2.1.31). As used herein, the term “beta-glucuronidase enzyme”, also referred to as “beta-glucuronidase” or “BGUS”, refers to an enzyme that hydrolyzes beta-glucuronide linkages. A BGUS enzyme used in an enzyme blend of the disclosure can be, for example, a wild type enzyme or a mutated enzyme. A “wild type” BGUS enzyme refers to the naturally occurring form of the enzyme. A “mutated” BGUS enzyme refers to a modified form of the enzyme in which one or more modifications, such as amino acid substitutions, deletions and/or insertions, have been made such that the amino acid sequence of the mutated BGUS enzyme differs from the wild type amino acid sequence. In one embodiment, a BGUS enzyme used in an enzyme blend of the disclosure is a recombinant BGUS enzyme. A “recombinant” BGUS enzyme refers to a genetically engineered form of a BGUS enzyme. In another embodiment, a BGUS enzyme used in an enzyme blend of the disclosure is a purified, naturally-occurring BGUS enzyme. A “purified, naturally-occurring” BGUS enzyme refers to an enzyme that has been extracted from a natural biological source.

The sequences of wild type BGUS enzymes from numerous species are known in the art. For example, the nucleotide sequence encoding wild type E. coli K12 strain BGUS is shown in NCBI Reference Sequence: NC_000913.2. The amino acid sequence of wild type human (Homo sapiens) BGUS (isoform 1 precursor) is shown in NCBI Reference Sequence NP_000172.2. The amino acid sequence of wild type mouse (Mus musculus) BGUS (precursor) is shown NCBI Reference Sequence NP_034498.1. The amino acid sequence of wild type Lactobacillus brevis BGUS is shown in Genbank Accession No. ACU21612.1. The amino acid sequence of wild type Staphylococcus sp. RLH1 BGUS is shown in Genbank Accession No. AAK29422.1. Furthermore, the sequences of a number of microbial BGUS enzymes are disclosed in U.S. Pat. No. 6,391,547 and EP Patent EP 1175495B, the entire contents of which, including the sequence listing, are incorporated herein by reference. Additionally, the nucleotide and amino acid sequence for BGUS from Brachyspira pilosicoli is disclosed in U.S. Patent Publication 20180067116, the entire contents of which, including the sequence listing, are incorporated herein by reference.

The sequences of variant BGUS enzymes from numerous species also are known in the art. Suitable variant BGUS enzymes are disclosed in, for example, US Patent Publication 20160090582 (issued as U.S. Pat. No. 9,920,306), US Patent Publication 20160237415 (issued as U.S. Pat. No. 9,719,075), and US Patent Publication 20170267985 (issued as U.S. Pat. No. 9,909,111), the entire contents of each of which, including the sequences, is expressly incorporated herein by reference. Additional variant BGUS enzymes have been described in the art, e.g., in Xiong, A-S. et al. (2007) Prot. Eng. Design Select. 20:319-325. Furthermore, a variant BGUS enzyme suitable for use in the invention is commercially available (IMCSzyme®, Integrated Micro-Chromatography Systems, LLC). Still further, additional chimeric and variant BGUS enzymes suitable for use in the invention are described further below.

Furthermore, cloning, recombinant expression and purification of various wild-type and variant BGUS enzymes from different species is described in Examples 1 and 2. The amino acid sequences for BGUS enzymes from Aspergillus oryzae (AoGUS), Aspergillus terreus (AtGUS), Bacteroides fragilis (BfGUS), Bacteroides uniformis (BuGUS), Brachyspira murdochii (BmGUS), Brachyspira pilosicoli (BpGUS), Clostridium perfringens (CpGUS), Escherichia coli (EcGUS), IMCSzyme® variant Escherichia coli K12 (EcElF), Eubacterium eligens (EeGUS), Homo sapiens (HsGUS), Lactobacillus brevis (LbLR2D), Mus musculus (MmGUS), Parabacteroides sp. (PmGUS), Staphylococcus sp. (StpGUS) and Streptococcus agalactiae (SaGUS) are shown in SEQ ID Nos: 1-16, respectively, and are aligned in FIG. 1 .

In one embodiment, at least one enzyme in the blend comprises an amino acid sequence at least 90% identical (or at least 95%, 96%, 97%, 98%, 99% or 100% identical) to one of the amino acid sequences shown in SEQ ID NOs: 1-10. In another embodiment, at least two enzymes in the blend comprise an amino acid sequence at least 80% identical (or at least 85%, 90%, 95%, 96%, 97%, 98%, 99% or 100% identical) to one of the amino acid sequences shown in SEQ ID NOs: 1-16. In yet another embodiment, at least three enzymes in the blend comprise an amino acid sequence at least 80% identical (or at least 85%, 90%, 95%, 96%, 97%, 98%, 99% or 100% identical) to one of the amino acid sequences shown in SEQ ID NOs: 1-16. In one embodiment, at least one of the enzymes in the blend comprises the amino acid sequence shown in SEQ ID NO: 9 (corresponding to the IMCSzyme® mutant Escherichia coli K12 BGUS enzyme).

For amino acid sequences, as used herein the term “% homology” or “% identity” indicates that when two sequences are aligned and compared, with appropriate amino acid insertions or deletions for optimal alignment, at least about 80% of the amino acids, usually at least about 85% or 90% or 95%, 96%, 97%, 98%, 99% or 100%, are identical between the two sequences. The percent identity between two sequences is a function of the number of identical positions shared by the sequences (i.e., % homology/identity=# of identical positions/total # of positions×100), taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences. The comparison of sequences and determination of percent identity between two sequences can be accomplished using a mathematical algorithm, non-limiting examples of which below are described below.

The percent identity between two amino acid sequences can also be determined using the algorithm of E. Meyers and W. Miller (CABIOS, 4:11-17 (1989)) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4. In addition, the percent identity between two amino acid sequences can be determined using the Needleman and Wunsch ((1970)J Mol. Biol. 48:444-453) algorithm which has been incorporated into the GAP program in the GCG software package (available at http://www.gcg.com), using either a Blossum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6. Other suitable means for determining percent identity between two amino acid sequences are well-established in the art.

Additional or alternative to the use of recombinant BGUS enzymes, a BGUS enzyme blend of the disclosure can comprise at least one purified, naturally-occurring BGUS enzyme (i.e., an enzyme that has been extracted from a natural biological source). Suitable natural biological sources for purification of a naturally-occurring BGUS enzyme include bacteria and mollusks (e.g., snail, abalone, limpet). In one embodiment, at least one enzyme in the blend is purified from a mollusk source. In one embodiment, the mollusk source is selected from the group consisting of Helix, Haliotis, Cornu and Patella. In one embodiment, the mollusk source is selected from the group consisting of Helix pomatia, Helix aspera, Haliotis rufescens and Patella vulgata. Naturally-occurring BGUS enzymes extracted from a natural biological source (e.g., snail, abalone, limpet) are commercially available in the art or can be purified using standard methodology known in the art.

In one embodiment, a BGUS enzyme blend of the disclosure comprises entirely recombinant BGUS enzymes. In another embodiment, a BGUS enzyme blend of the disclosure comprises entirely purified, naturally-occurring BGUS enzymes. In yet another embodiment, a BGUS enzyme blend of the disclosure comprises a combination of recombinant BGUS enzymes and naturally-occurring BGUS enzymes.

In certain embodiments, each enzyme in the blend is purified to at least 80% (more preferably at least 85%, 90%, 95%, 96%, 97%, 98%, 99% or 100%) homogeneity. In certain embodiments, the BGUS enzyme blend is substantially free of other non-BGUS proteins. As used herein, “substantially free” refers to less than 10%, preferably less than 5%, 4%, 3%, 2% or even more preferably less than 1% of contamination non-BGUS proteins.

BGUS enzyme blends can be prepared such that the blend is capable of catalyzing a desired range of substrates (e.g., for a particular clinical or forensic purpose). In one embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated metabolites of drugs comprising opiates, synthetic opioids, anti-depressants and benzodiazepines. In one embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated opiates comprising morphine-3-β-D-glucuronide, hydromorphone-3-β-D-glucuronide, oxymorphone-3-β-D-glucuronide, codeine-6-β-D-glucuronide and dihydrocodeine-6-β-D-glucuronide. In one embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated opioids comprising buprenorphine glucuronide, norbuprenorphine glucuronide and tapentadol glucuronide. In one embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated anti-depressants comprising O-desmethylvenlafaxine glucuronide and amitriptyline-N-β-D-glucuronide. In one embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated benzodiazepines comprising temazepam glucuronide, oxazepam glucuronide and lorazepam glucuronide. In yet another embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises the panel of fourteen substrates set forth in Example 4.

A. Chimeric BGUS Enzymes

In one embodiment, one or more of the enzymes used in an enzyme blend of the disclosure is a chimeric BGUS enzyme. Suitable chimeric BGUS enzymes are described in detail in U.S. Provisional Application No. 62/742,779, filed Oct. 8, 2018, the entire contents of which is hereby incorporated by reference.

In one embodiment, the chimeric beta-glucuronidase (BGUS) enzyme comprises an N-terminal sugar-binding/Ig-like domain (SBI domain) and a C-terminal TIM-Barrel domain (TIMB domain) comprising a Loop 1 domain, wherein the chimeric BGUS enzyme comprises:

(a) an SBI domain from a first BGUS enzyme and a TIMB domain and Loop 1 domain from a second BGUS enzyme; or

(b) an SBI domain from a first BGUS enzyme, a TIMB domain from a second BGUS enzyme, and a Loop 1 domain from the first BGUS enzyme; or

(c) an SBI domain and a TIMB domain from a first BGUS enzyme and a Loop 1 domain from a second BGUS enzyme.

Non-limiting examples of chimeric BGUS enzymes having an SBI domain from a first BGUS enzyme and a TIMB domain and Loop 1 domain from a second BGUS enzyme include those having any of the amino acid sequences shown in SEQ ID NOs: 17-24. In a preferred embodiment, the chimeric enzyme is Rxn3, which has the amino acid sequence shown in SEQ ID NO: 19.

Non-limiting examples of chimeric BGUS enzymes having an SBI domain from a first BGUS enzyme, a TIMB domain from a second BGUS enzyme, and a Loop 1 domain from the first BGUS enzyme include those having any of the amino acid sequences shown in SEQ ID NOs: 25-30.

Non-limiting examples of chimeric BGUS enzymes having an SBI domain and a TIMB domain from a first BGUS enzyme and a Loop 1 domain from a second BGUS enzyme include those having any of the amino acid sequences shown in SEQ ID NOs: 31-36.

In another embodiment, the chimeric BGUS enzyme comprises a TIM-Barrel domain (TIMB domain) comprising a Counter-loop domain and a Loop 1 domain, wherein the chimeric BGUS enzyme comprises:

-   -   (a) a TIMB domain from a first BGUS enzyme, a Counter-loop         domain from a second BGUS enzyme and a Loop 1 domain from the         first BGUS enzyme; or     -   (b) a TIME domain from a first BGUS enzyme, a Counter-loop         domain from the first BGUS enzyme and a Loop 1 domain from a         second BGUS enzyme; or     -   (c) a TIMB domain from a first BGUS enzyme, a Counter-loop         domain from a second BGUS enzyme and a Loop 1 domain from the         second BGUS enzyme.

Non-limiting examples of chimeric BGUS enzymes having the aforementioned TIME domain, Counter-loop domain, Loop 1 domain swaps include those having any of the amino acid sequences shown in SEQ ID NOs: 37-46.

B. Variant BGUS Enzymes

In one embodiment, one or more of the enzymes used in an enzyme blend of the disclosure is a variant BGUS enzyme having one or more amino acid substitutions as compared from a parental BGUS enzyme from which the variant BGUS enzyme is derived. Suitable variant BGUS enzymes are described in detail in U.S. Provisional Application No. 62/742,779, filed Oct. 8, 2018, the entire contents of which is hereby incorporated by reference.

In one embodiment, the variant BGUS enzyme comprises an amino acid sequence at least 80% homologous to an amino acid sequence shown in SEQ ID NOs: 1-46 and comprises at least one amino acid substitution, as compared to the parental BGUS enzyme, at at least one amino acid position corresponding to F294, T295, I450, Q451, A452 and/or G563 of SEQ ID NO: 5.

Non-limiting examples of variant BGUS enzymes having a single amino acid substitution at a position corresponding to F294, T295, I450, Q451, A452 and/or G563 of SEQ ID NO: 5 include those having any of the amino acid sequences shown in SEQ ID NOs: 47-100.

Non-limiting examples of variant BGUS enzymes having double amino acid substitutions at two position corresponding to F294, T295, I450, Q451, A452 and/or G563 of SEQ ID NO: 5 include those having any of the amino acid sequences shown in SEQ ID NOs: 101-117.

In another embodiment, the variant BGUS enzyme comprises an amino acid sequence at least 80% homologous to an amino acid sequence shown in SEQ ID NOs: 1-46 and comprises at least one cysteine substitution, as compared to the parental BGUS enzyme, at at least one amino acid position corresponding to Q8, S73, P489, Q570 or K588 of SEQ ID NO: 10.

Non-limiting examples of variant BGUS enzymes having at least one cysteine substitution at a position corresponding to Q8, S73, P489, Q570 or K588 of SEQ ID NO: 10 include those having any of the amino acid sequences shown in SEQ ID NOs: 118-120.

III. Formulations of Enzyme Blends

The compositions of the disclosure comprising an enzyme blend can be included in formulations that contain additional substances and/or that are formulated in a particular way. For example, the formulations of the disclosure can be either liquid (aqueous) or lyophilized (freeze-dried). Liquid formulations typically allow for maintenance of enzymatic activity even after cycles of freezing/thawing. Lyophilized formulations typically maintain enzymatic activity over a wide temperature range, including high temperatures. Typically, a formulation comprises the enzyme blend composition and at least one excipient. Non-limiting examples of excipients that can be included in a formulation include water, salts, buffers, sugars and amino acids. Certain BGUS enzyme formulations have been described in the art, such as in PCT Application No. PCT/US2017/14387, the entire contents of which is expressly incorporated herein by reference.

Aqueous and lyophilized formulations can be prepared using methods well established in the art. Typically, an aqueous formulation is prepared by combining the enzymes and the excipient(s) at the desired concentrations. A lyophilized formulation can be made by freeze-drying the aqueous formulation using techniques well established in the art.

In certain embodiments, one or more sugars are used in the formulation. In one embodiment, the sugar is a polyol. In certain embodiments, the sugar(s) used in the formulation is selected from the group consisting of sucrose, sorbitol, xylitol, glycerol, 2-hydroxypropyl-β-cyclodextrin and α-cyclodextrin. In a preferred embodiment, the sugar is sucrose.

In certain embodiments, the sugar is present in the formulation at a concentration of at least 10 mM, or at least 25 mM or at least 50 mM or at least 100 mM. In other embodiments, the sugar is present in the formulation at a concentration of 10-1000 mM, or 25-500 mM or 50 mM-250 mM or 50 mM-500 mM or 50 mM-1000 mM. In other embodiments, the sugar is present in the formulation at a concentration of 50 mM or 75 mM or 100 mM or 200 mM or 250 mM or 300 mM or 400 mM or 500 mM or 600 mM or 700 mM or 750 mM or 800 mM or 900 mM or 1000 mM.

In certain embodiments, one or more amino acids (e.g., beta-alanine, L-histidine) is present in the formulation at a concentration of at least 25 mM or at least 50 mM. In other embodiments, the amino acid(s) is present in the formulation at a concentration of 25-500 mM or 50 mM-250 mM or 50 mM-500 mM. In other embodiments, the amino acid(s) is present in the formulation at a concentration of 25 mM or 30 mM or 40 mM or 50 mM or 75 mM or 100 mM or 200 mM or 250 mM or 300 mM or 400 mM or 500 mM.

In certain embodiments, each BGUS enzyme in the enzyme blend is present in the formulation at a concentration of at least 0.1 mg/mL. In certain embodiments, at least one BGUS enzyme is present in the formulation at a concentration of at least 1 mg/mL or at least 2.5 mg/mL or at least 5 mg/mL or at least 10 mg/mL. In other embodiments, at least one BGUS enzyme is present in the formulation at a concentration of 1-10 mg/mL or 1-5 mg/mL or 2.5-10 mg/mL or 2.5-5 mg/mL. In other embodiments, at least one BGUS enzyme is present in the formulation at a concentration of 1 mg/mL or 2 mg/mL or 3 mg/mL or 4 mg/mL or 5 mg/mL or 6 mg/mL or 7 mg/mL or 8 mg/mL or 9 mg/mL or 10 mg/mL.

In certain embodiments, at least one BGUS enzyme in the formulation has an enzymatic activity of at least 5,000 Units/mL or 5,000 Units/mg, more preferably at least 10,000 Units/mL or 10,000 Units/mg, even more preferably at least 25,000 Units/mL or 25,000 Units/mg and even more preferably 50,000 Units/mL or 50,000 Units/mg. The specific activity of the enzyme in the preparation, in Units/mL or Units/mg, can be determined using a standardized glucuronide linkage hydrolysis assay using phenolphthalein-glucuronide as the substrate. The standardization of the specific activity of BGUS has been well established in the art. Thus, 1 Fishman unit of BGUS activity is defined as an amount of enzyme that liberates 1 μg of phenolphthalein from phenolphthalein-glucuronide in 1 hour. An exemplary standardized assay that can be used to determine the specific activity (in Units/mL or Units/mg) of an enzyme preparation is described in further detail in Example 3. The skilled artisan will appreciate that other protocols for the enzyme assay are also suitable (e.g., such as those described by Sigma Aldrich Chemical Co.).

In one embodiment, the formulation is free of detergents, such as surfactants (e.g., Tween compounds and the like). Since the presence of detergents in a BGUS formulation can interfere with mass spectrometry (MS) analysis, the lack of detergent(s) in the formulation of the invention imparts the advantage that the formulation can be used directly in analysis of biological samples to be assayed by MS.

In one embodiment, the formulation is free of polymers (e.g., synthetic polymers and the like). Since the presence of polymers in a BGUS formulation can interfere with mass spectrometry (MS) analysis, the lack of polymer(s) in the formulation of the invention imparts the advantage that the formulation can be used directly in analysis of biological samples to be assayed by MS.

In one embodiment, the formulation is an aqueous (liquid) formulation. In another embodiment, the formulation is a lyophilized (freeze-dried) formulation.

Packaged formulations, comprising a formulation of the disclosure and a container, are also encompassed. Non-limiting examples of suitable containers for use in a packed formulation include, bottles, tubes, vials, ampules and the like. Preferably, the container is glass or plastic, although other suitable materials are known in the art. Non-limiting examples of suitable instruction media include labels, pamphlets, inserts, and digital media.

IV. Methods of Using Enzyme Blends

The enzyme blend compositions and formulations of the disclosure can be used to catalyze a range of substrates according to the type of enzyme (EC number) used in the blend. Since the enzyme blends exhibit a greater substrate range (and may exhibit a greater pH range and/or temperature range) than each individual enzyme included in the blend, the methods of using the enzyme blends as described herein allow for more efficient catalysis of a range of substrates as compared to use of individual enzymes.

Accordingly, in one embodiment, the invention pertains to a method of catalyzing a range of substrates, the method comprising contacting the range of substrates with an enzyme blend composition or the formulation of the disclosure under conditions such that catalysis of the range of substrates occurs. In various embodiments, the range of substrates comprises at least two substrates, at least three substrates, at least four substrates, at least five substrates, at least six substrates, at least seven substrates, at least eight substrates, at least nine substrates, at least ten substrates, at least eleven substrates, at least twelve substrates, at least thirteen substrates, at least fourteen substrates or at least fifteen substrates. In various embodiments, the enzyme blend comprises at least two enzymes, at least three enzymes, at least four enzymes, at least five enzymes, at least six enzymes, at least seven enzymes, at least eight enzymes, at least nine enzymes, at least ten enzymes, at least eleven enzymes, at least twelve enzymes, at least thirteen enzymes, at least fourteen enzymes or at least fifteen enzymes.

In one embodiment, the enzyme blend is a BGUS enzyme blend. Accordingly, in another embodiment, the invention pertains to a method of hydrolyzing a range of substrates comprising a glucuronide linkage, the method comprising contacting the range of substrates with a BGUS enzyme blend composition or formulation of the disclosure under conditions such that hydrolysis of the glucuronide linkage in the range of substrates occurs. In various embodiments, the range of substrates comprises at least two BGUS substrates, at least three BGUS substrates, at least four BGUS substrates, at least five BGUS substrates, at least six BGUS substrates, at least seven BGUS substrates, at least eight BGUS substrates, at least nine BGUS substrates, at least ten BGUS substrates, at least eleven BGUS substrates, at least twelve BGUS substrates, at least thirteen BGUS substrates, at least fourteen BGUS substrates or at least BGUS fifteen substrates. In one embodiment, the range of BGUS substrates includes codeine-6-β-D-glucuronide. In various embodiments, the enzyme blend comprises at least two BGUS enzymes, at least three BGUS enzymes, at least four BGUS enzymes, at least five BGUS enzymes, at least six BGUS enzymes, at least seven BGUS enzymes, at least eight BGUS enzymes, at least nine BGUS enzymes, at least ten BGUS enzymes, at least eleven BGUS enzymes, at least twelve BGUS enzymes, at least thirteen BGUS enzymes, at least fourteen BGUS enzymes or at least fifteen BGUS enzymes.

These BGUS enzyme blend methods can be used, for example, for clinical purposes, for forensic purposes, for industrial manufacturing purposes or for agricultural purposes. These methods are particularly useful for analyzing bodily samples for the presence of drugs through detection of the glucuronide detoxification products of the drugs, e.g., for clinical or forensic purposes. Additionally, beta-agonists have been used in meat husbandry, since they can promote muscle growth instead of fat growth in animals (see e.g., J. Animal Sci. (1998) 76:195-207). Thus, the BGUS enzyme formulations also can be used for agricultural purposes in detecting beta-agonist residues in meat products.

In one embodiment, the range of substrates comprises opiate glucuronides. Non-limiting examples of suitable opiate glucuronide substrates include morphine-3-β-D-glucuronide, morphine-6-β-D-glucuronide, codeine-6-β-D-glucuronide, hydromorphone-3-β-D-glucuronide, oxymorphone-3-β-D-glucuronide, and combinations thereof.

In another embodiment, the range of substrates comprises benzodiazepine glucuronides. Non-limiting examples of suitable benzodiazepine glucuronide substrates include the glucuronides of oxazepam, lorazepam, temazepam, and alpha-hydroxy-alprazolam.

Other suitable ranges of substrates include the glucuronides of buprenorphine, norbuprenorphine, 11-nor-Δ9-tetrahydrocannabinol-9-carboxylic acid, testosterone, androsterone, tapentadol, cyclobenzaprine, amitriptyline and combinations thereof.

In another embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated metabolites of drugs comprising opiates, synthetic opioids, anti-depressants and benzodiazepines. In another embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated opiates comprising morphine-3-β-D-glucuronide, hydromorphone-3-β-D-glucuronide, oxymorphone-3-β-D-glucuronide, codeine-6-β-D-glucuronide and dihydrocodeine-6-β-D-glucuronide. In another embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated opioids comprising buprenorphine glucuronide, norbuprenorphine glucuronide and tapentadol glucuronide. In another embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated anti-depressants comprising O-desmethylvenlafaxine glucuronide and amitriptyline-N-β-D-glucuronide. In another embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises glucuronidated benzodiazepines comprising temazepam glucuronide, oxazepam glucuronide and lorazepam glucuronide. In yet another embodiment, the range of substrates catalyzed by the BGUS enzyme blend comprises the panel of fourteen substrates set forth in Example 4.

In one embodiment, the range of substrates comprises at least one beta-agonist (e.g., for meat product analysis). Non-limiting examples of suitable beta-agonist glucuronidated substrates include clenbuterol, ractopamine and salbutamol.

In one embodiment, the range of substrates is in a sample of blood, urine, tissue or meconium obtained from a subject. The methods of the invention can be used on a variety of different bodily samples. Non-limiting examples of suitable bodily samples include blood, urine, tissue or meconium obtained from a subject. For meat product analysis, the bodily sample can be a meat sample. Bodily samples can be obtained, stored and prepared for analysis using standard methods well established in the art.

Following hydrolysis by the enzyme, the cleavage products in the sample can be analyzed by standard methodologies, such as high performance liquid chromatography (HPLC), gas chromatography (GC) and/or mass spectrometry (MS). Such approaches for analysis of bodily samples for the presence of drugs are well established in the art. For example, a completely automated workflow for the hydrolysis and analysis of urine samples by LC-MS/MS, which can be applied using the variant enzymes of the invention for hydrolysis, is described in Cabrices, O. G. et al., GERSTEL AppNote AN/2014/4-7. Additional liquid chromatography and tandem mass spectrometry (LC-MS/MS) methodologies suitable for use with the invention are described in Sitasuwan et al. (2016) J. Analytic. Toxicol. 40:601-607. Methods for detecting beta-agonist residues in meat products using UPLC-MS/MS have also been described (www.waters.com/webassets/cms/library/docs/720004388en.pdf).

V. Optimization and Preparation of Enzyme Blends

The disclosure provides approaches and methods for optimizing the ratios of two or more enzymes in an enzyme blend such that the blend exhibits enhanced properties as compared to each enzyme alone. Thus, the disclosure allows for the preparation of an optimized enzyme blend for use with a panel (range) of substrates, which poses a significantly greater technical challenge than enzymatic cleavage of a single substrate alone.

For a single specific substrate, such as a glucuronidated drug molecule, under defined conditions of temperature and pH, within a panel of enzymes there will be one enzyme that has superior hydrolysis capacity, relative to all the other enzymes. Hydrolysis capacity might be defined as either the rate at which it hydrolyzes the substrate (highest rate) or the dose required to hydrolyze a certain amount within a certain time under defined conditions (minimum dose). For a single enzyme-substrate pair, these values are related and reciprocal.

However, in the field of clinical and forensic drug testing, laboratories want to test for a panel of drug molecules that may include tens to hundreds of substrates. As discussed above, for each substrate there will be one superior enzyme, but this may be a different enzyme for each substrate. Thus, the situation becomes more complex when there are at least two substrates to hydrolyze and at least two enzymes available for the blend. The disclosure provides two methods for calculating optimal blends of enzymes to hydrolyze a panel of substrates under different defined conditions, each having a different intent with respect to the strategy used for optimization, as discussed further below.

The first approach (referred to herein as Model 1) identifies the ratios to be used in a blend to achieve the fastest weighted-average kinetic for all substrates. Thus, since Model 1 is based on the use of a weighted average of activity for all substrates, Model 1 provides ratios for a blend that results in “good” activity against all substrates in the panel under specified conditions (e.g., pH and temperature), while potentially somewhat compromising the “best” activity of one or more enzymes in the blend against one or more substrates in the panel.

The second approach (referred to herein as Model 2) identifies the ratios to be used in a blend to achieve the minimum dose for complete hydrolysis of all substrates in the panel, including the most recalcitrant substrate, in a specified time. Thus, since Model 2 is based on the complete hydrolysis of all substrates in the panel within a specified time, it provides the optimal ratios for achieving hydrolysis of the “weakest link” (i.e., the most recalcitrant substrate) as well as the other substrates in the panel. Thus, Model 2 can be a more practical model for use with a panel of substrates that includes at least one recalcitrant (i.e., difficult to cleave) substrate. For example, it is known in the art that among the glucuronide substrates typically examined in clinical and forensic drug testing, codeine-6-β-D-glucuronide is more recalcitrant to cleave by BGUS than other substrates (see e.g., Lin et al. (1994) J. Anal. Toxicol. 18:129-133; Wang et al. (2006) J. Anal. Toxicol. 30:570-575). Thus, for substrate panels that include codeine-6-β-D-glucuronide, Model 2 is an appropriate model to use to obtain the optimal ratios for a blend to achieve complete hydrolysis of codeine-6-β-D-glucuronide within a specified time.

Comparing the two models, Model 2 is more strictly dependent upon the particular substrates used to define the blend and also includes time as a pre-condition, whereas Model 1 is open ended and defined only by pH and temperature (which are defined pre-conditions for both models). For particular enzymes and substrates under specified pH and temperature conditions, the ordinarily skilled artisan will appreciate that the optimal blend ratios may differ for the optimized weighted average activity (Model 1) as compared to the minimum dose (Model 2). Moreover, given the disclosure herein, the ordinarily skilled artisan can readily choose the appropriate model to use and determine the appropriate ratios to select for a desired enzyme blend for use with a particular panel of substrates. For example, FIGS. 10A-B and 11A-B show comparisons of the optimal ratios for two different enzyme blends using the two different models across of range of pH and temperature conditions.

Methodologies for optimizing enzyme blends of the disclosure are also described in detail in Examples 3-5. To prepare a blend of enzymes having optimized activity against a range of substrates, the specific activity of each enzyme on each substrate at a given temperature and pH must either be already known or determined experimentally. For BGUS enzymes, a standard enzymatic activity assay, using phenolphthalein-β-D-glucuronide (PTGlcU) as the substrate, is described in Example 3 and can used to determine enzyme activity across a range of pH and/or a range of temperatures. Thus, this type of assay (or other suitable BGUS activity assay known in the art) can be used to determine the optimum pH and/or temperature range for each BGUS enzyme of interest individually against a standard substrate. Moreover, similar suitable enzymatic activity assays for other classes of enzymes (e.g., sulfatases) also are well-established in the art.

Furthermore, as described in detail in Example 4, activity surface plots can be prepared for each BGUS enzyme of interest individually using a panel of substrates, thereby determining activity across a range of pH and temperatures for each substrate in the panel. One approach is to measure the activity of each individual enzyme against a panel of substrates at the enzyme's optimal pH and the preferred assay temperature, then multiply these values over the pH and temperature profiles (either actual or modeled) to create activity surfaces for each enzyme/substrate pair. Alternatively, these values can be measured directly by experiment. FIG. 4 shows the activity surface plots for BpGUS on fourteen different glucuronidated drug substrates tested across the indicated pH and temperature ranges. FIG. 5 shows the activity surface plots for EeGUS on fourteen different glucuronidated drug substrates tested across the indicated pH and temperature ranges.

Data from such activity surface plots then can be used to optimize enzyme blends for particular conditions, as described in detail in Example 5. Two different models for determining an optimal enzyme blend are provided. Either or both models can be used to determine an optimal enzyme blend for a particular set of enzymes to be included in a blend.

The first model (referred to herein as Model 1) for optimizing enzyme blends involves maximizing the sum of the blended activity. In this model, to determine the activity of a blend at a given pH (pH_(m)) and temperature (T_(n)), the fractional activities for all enzymes for a particular substrate are summed. For the case where these activities have been measured directly for a given enzyme, Equation 1a (below) describes how this is done. For the case where activities are adjusted based on either a model substrate, where it has been measured for the whole activity surface, or where the activity surface itself has been modeled mathematically from a single point, the summed activities are calculated with Equation 1b (below). The product of summed activities for all substrates, theta (0), is then calculated with Equation 2 (below). The x^(th) root of theta is called the root sum activity. Using optimization software, the optimal blend for a particular pH and T is determined by maximizing theta, which represents the activity across all substrates at pH_(m) and T_(n). To determine the optimal blend for a wider pH and/or temperature range, the activity products for each point on the reaction surface are averaged and the fractional activities optimized by maximizing the average activity.

$\begin{matrix} {A_{1},{pH_{m}},{T_{n} = {\left( {{f_{a} \cdot A_{a}},{pH_{m}},T_{n}} \right) + \left( {{f_{b} \cdot A_{b}},{pH_{m}},T_{n}} \right) + \ldots\mspace{14mu} + \left( {{f_{z} \cdot A_{z}},{pH_{m}},T_{n}} \right)}}} & \underset{\_}{{Equation}\mspace{14mu} 1a} \\ {{{{A_{1},{pH_{m}},{T_{n} =}}\quad}\left( {{f_{a} \cdot A_{a} \cdot {\overset{\hat{}}{A}}_{a,{PTGlcU}}},{pH_{m}},T_{n}} \right)} + \left( {{f_{b} \cdot A_{b} \cdot {\overset{\hat{}}{A}}_{b,{PTGlcU}}},{p\; H_{m}},T_{n}} \right) + \ldots\mspace{14mu} + \left( {{f_{z} \cdot A_{z} \cdot {\overset{\hat{}}{A}}_{z,{PTGlcU}}},{p\; H_{m}},T_{n}} \right)} & \underset{\_}{{Equation}\mspace{14mu} 1b} \\ {{\Theta\; p\; H_{m}},{T_{n} = {{\quad\quad}{\left( {A_{1},{pH_{m}},T_{n}} \right) \cdot {\quad{{\left( {A_{2},{pH_{m}},T_{n}} \right) \cdot \left( {A_{3},{pH_{m}},T_{n}} \right)}\ldots\mspace{14mu}\left( {A_{x},{pH_{m}},T_{n}} \right)}}}}}} & \underset{\_}{{Equation}\mspace{14mu} 2} \end{matrix}$ Where:

-   -   A=activity, typically expressed in moles product per gram         protein per unit time (for example: pmol·mg⁻¹·min⁻¹), for the         enzyme, substrate, and/or condition indicated by the subscripts;     -   Â=normalized activity based on the indicated model substrate         and/or activity surface model. For this example, PTGlcU is the         model substrate. However, the ordinarily skilled artisan will         appreciate that other model substrates can be used, and this         example is not limited to PTGlcU and does not exclude the         possibility of using other model substrates;     -   a, b, . . . z=different enzymes;     -   1, 2, . . . x=different substrates;     -   m and n denote particular pH and temperature, respectively;     -   f=fraction of the subscripted enzyme in a blend. f_(a)+f_(b)+ .         . . +f_(z)=1; 0≤f≤1 for all f.

A blend may be further optimized for a contiguous range of pH and temperature by averaging theta across the range and adjusting the fractions to maximize the average.

Accordingly, in another aspect, the disclosure pertains to a method of preparing an enzyme blend that is optimized for a given temperature, pH, and substrate target range, wherein each enzyme within the blend has the same EC number and wherein the specific activity of each enzyme on each substrate for the given temperature and pH is known, the method comprising:

-   -   (i) calculating the fractional activity of each enzyme based on         the fraction of enzyme in the blend multiplied by the specific         activity of the enzyme under the given conditions of temperature         and pH;     -   (ii) summing the fractional activities for all enzymes for each         substrate;     -   (iii) multiplying the total activities at the given temperature         and pH for each substrate in the target range;     -   (iv) maximizing the grand total activity at the given         temperature and pH by adjusting the fractions of enzyme in the         blend, to thereby obtain an optimized fraction for each enzyme         in the blend; and     -   (v) combining the optimized fraction of each enzyme together to         thereby prepare the enzyme blend.         Steps (i) and (ii) above can be performed, for example, using         Equation 1a or Equation 1b set forth above.         Step (iii) above can be performed, for example, using Equation 2         set forth above.         In one embodiment, the enzyme blend is further optimized for a         broader temperature and pH range by summing the grand totals         from each temperature and pH condition and adjusting the enzyme         fractions to maximize the summed grand totals. In one         embodiment, the grand total activities are weighted in         proportion to the probability that samples to undergo enzymatic         hydrolysis will represent a particular temperature and pH         condition.

The second model (referred to herein as Model 2) for optimizing enzyme blends utilizes the minimal enzyme amounts required for complete hydrolysis at specified conditions (pH and temperature for a defined length of time) and thus is also referred to as a minimal enzyme requirement model. In this model, to determine an amount of enzyme blend and a blend ratio to completely hydrolyze all fourteen substrates at a given pH (pH_(m)) and temperature (T_(n)), the total amount of blended enzyme to achieve a complete hydrolysis of each substrate at a given blend ratio and at a given time (t) is solved using Equation 3. To determine the optimal blend for minimal enzyme amount requirement at a given pH and temperature, the blend ratio is solved by minimizing the total amount of blended enzyme required to complete hydrolysis for all fourteen substrates within a given incubation time.

$\begin{matrix} {{{{\lambda_{1}pH_{m}},{T_{n} =}}\quad}\left( \frac{{pmol}_{1}}{\begin{matrix} \left( {\left( {{f_{a} \cdot A_{a}},{p\; H_{m}},T_{n}} \right) + \left( {{f_{b} \cdot A_{b}},{pH_{m}},T_{n}} \right) + \ldots\mspace{14mu} +} \right. \\ {\left. \left( {{f_{z} \cdot A_{z}},{p\; H_{m}},T_{n}} \right) \right) \cdot t} \end{matrix}} \right)} & {{Equation}\mspace{14mu} 3} \end{matrix}$ Where

-   -   λ=total amount of blended enzyme expressed in milligrams;     -   pmol=total amount of substrate expressed in picomoles;     -   a, b, . . . z=different enzymes;     -   1, 2, . . . x=different substrates; m and n denote particular pH         and temperature, respectively;     -   f=fraction of the subscripted enzyme in a blend. f_(a)+f_(b)+ .         . . +f_(z)=1; 0≤f≤1 for all f,     -   t=incubation time desired to complete hydrolysis expressed in         minutes.         In one embodiment, each enzyme within the blend has the same         Enzyme Commission (EC) number. In one embodiment, each enzyme in         the blend is a beta-glucuronidase enzyme (EC number 3.2.1.31).         In one embodiment, each enzyme in the blend is a sulfatase (EC         number 3.1.6.1).         Once the components of an enzyme blend are optimized (i.e., the         fraction of each enzyme to be included in the blend, for a         desired range of substrates using a desired set of conditions,         such as pH and temperature ranges) as described above, the         enzyme blend is prepared simply by combining the enzymes (i.e.,         at least two or more enzymes) together in the appropriate         amounts (i.e., appropriate fractions, expressed for example as %         of the total protein mass and/or the % of the total enzyme mass         for each enzyme to be included in the blend). Accordingly, in         another aspect, the disclosure pertains to a method of preparing         an enzyme blend, the method comprising combining at least two         enzymes together, wherein the ratio (i.e., fraction) of each         enzyme in the blend is optimized such that the blend exhibits         enhanced properties as compared to each enzyme alone (e.g.,         synergistic properties, such as any of those described herein).         The ratio (i.e., fraction) of each enzyme in the blend can be         determined according to any of the methodologies described         herein for optimizing the enzyme blend (e.g., Model 1 or Model         2, described herein). Additional excipients can be added to the         enzyme blend to prepare a formulation of the disclosure, as         described further in subsection IV above. Furthermore, the         enzyme blend composition or formulation can be prepared as an         aqueous solution or a lyophilized preparation, as described in         subsection IV above.

The present invention is further illustrated by the following examples, which should not be construed as further limiting. The contents of Sequence Listing, figures and all references, patents and published patent applications cited throughout this application are expressly incorporated herein by reference.

EXAMPLES Example 1: Gene Synthesis, Cloning and Protein Expression

In this example, genes for various beta-glucuronidase enzymes were synthesized cloned and expressed. The DNA sequence coding for a protein sequence can be reconstructed from the protein sequence by standard methods well known in the art using the genetic code. For example, amino acid sequences for BGUS enzymes from Aspergillus oryzae (AoGUS), Aspergillus terreus (AtGUS), Bacteroides fragilis (BfGUS), Bacteroides uniformis (BuGUS), Brachyspira murdochii (BmGUS), Brachyspira pilosicoli (BpGUS), Clostridium perfringens (CpGUS), Escherichia coli (EcGUS), IMCSzyme® variant Escherichia coli K12 (EcElF), Eubacterium eligens (EeGUS), Homo sapiens (HsGUS), Lactobacillus brevis (LbLR2D), Mus musculus (MmGUS), Parabacteroides sp. (PmGUS), Staphylococcus sp. (StpGUS) and Streptococcus agalactiae (SaGUS) are shown in SEQ ID Nos: 1-16, respectively, and can be used to design appropriate DNA sequences coding for the enzymes. Additionally, FIG. 1 shows the amino acid sequence alignment for these sixteen BGUS enzymes that are shown in SEQ ID Nos: 1-16.

Typically, the enzyme-encoding DNA sequence is synthesized with consideration for the codon bias of the expression host, an approach also well established in the art. Using such methods, genes for EcEiF, AoLi-3, AtLi-20, BpGUS and EeGUS were synthesized with a codon bias compatible for expression in Escherichia coli host cells. The genes were cloned into plasmid vectors, placed under the control of an inducible promoter and expressed in a bacterial strain supportive of the construct, all using standard recombinant DNA technology. Enzymes were expressed intracellularly, the cells were lysed by a combination of physical and chemical means, and the lysates clarified by centrifugation. The lysates were then adjusted with buffer compatible with subsequent purification steps.

Example 2: Protein Purification and Acquisition

In this example, the cloned BGUS enzymes from Example 1 were further purified. Recombinant enzymes were purified by standard chromatography techniques known to those skilled in the art, on an ÄKTA™ Pure FPLC. Protein elution was monitored by measuring the absorbance at 280 nm, and protein purity was analyzed by standard SDS-PAGE and protein concentration was determined with the Bradford protein assay. The SDS-PAGE analysis revealed purified protein bands of the expected molecular weight for the BGUS enzymes applied to the gel, demonstrating effective purification of the recombinant enzymes. A representative SDS-PAGE gel for purified recombinant BpGUS and EeGUS is shown in FIG. 2 . BpGUS is shown in lane 1, EeGUS is shown in lane 2 and molecular weight markers (in kD) are shown in the lane marked M. Protein purity by gel quantification was calculated using ImageQuant IL 1D v8.1 software, which indicated 94% purity for BpGUS and 93% purity for EeGUS.

Example 3: Activity Measurements Using Phenolphthalein-β-D-Glucuronide

In this example, activity of a panel of recombinant BGUS enzymes was measured using the substrate phenolphthalein-β-D-glucuronide (PTGlcU), a standard substrate for monitoring and reporting BGUS activity, across a range of pH.

The pH profile for each recombinant enzyme was determined using a buffer system described in the art (Ellis and Morrison (1982) Methods Enzymol. 87:405-426), with phenolphthalein-β-D-glucuronide as the substrate. All enzymes were used at the same protein concentration with 1.0 mM PTGlcU in 10% ethanol. For set up, 25 μL of enzyme and 25 μL PTGlcU were mixed in a 96-well place format at room temperature and the reactions were stopped after 10 minutes by addition of 150 μL 0.2 M glycine, pH 10.4. The buffer pH range tested was from pH 4.5-8.0. Thirty minutes after stopping the reaction, the absorbance of each well was read at 540 nm. The pH profiles of each recombinant enzyme (BpGUS, EeGUS, EcEiF, AoGUS and AtGUS) are shown in FIG. 3 .

In summary, each enzyme had 80% or greater activity in the optimum pH ranges shown below in Table 1:

TABLE 1 pH Optimum Ranges for Recombinant BGUS Enzymes BGUS Enzyme Optimum pH Range BpGUS pH 7.0-7.5 EeGUS pH 5.0-6.5 EcE1F pH 6.5-7.5 AoGUS pH 4.5-5.5 AtGUS pH 5.0-5.5 Rxn3 pH 4.5-7.0

Example 4: Activity Surfaces with a Panel of Multiple Drug-Glucuronides

In this example, activity surface plots were prepared for beta-glucuronidases using a panel of substrates, determining activity across a range of pH and temperatures.

Each recombinant beta-glucuronidase was used to deconjugate fourteen glucuronidated drugs frequently tested in urine drug-testing applications. The substrates represent a wide variety of drug classes, such as opiates, synthetic opioids, benzodiazepines, and anti-depressants. The substrates included morphine-3-β-D-glucuronide (M3G), oxymorphone-3-β-D-glucuronide (OM3G), hydromorphone-3-β-D-glucuronide (HM3G), codeine-6-β-D-glucuronide (C6G), dihydrocodeine-6-β-D-glucuronide (DHC6G), buprenorphine-3-β-D-glucuronide (BUP glue), norbuprenorphine-3-β-D-glucuronide (NBUP glue), tapentadol glucuronide (TAP glue), O-desmethyltramadol glucuronide (ODT glue), O-desmethylvenlafaxine glucuronide (ODV glue), amitriptyline-N-β-D-glucuronide (AMT glue), oxazepem glucuronide (OXZ glue), lorazepam glucuronide (LOR glue) and temazepam glucuronide (TEM glue). The substrates were fortified in synthetic urine at a concentration equivalent to 500 ng/mL when liberated.

The hydrolysis buffer used was 0.2 M sodium acetate, pH ranged from 4.0 to 7.0 with 0.5 increments. The internal standard solution was prepared at 1 μg/mL of each deuterated drug standard in methanol. 50 μL of urine containing the fourteen substrates was mixed with 150 μL of hydrolysis buffer, 20 μL enzyme solution (five different dilutions) and 10 μL internal standard solution. The incubations were performed at 20° C., 25° C., 30° C., 35° C. or 40° C. for 15 minutes. Samples were extracted using dispersive pipette extraction tips with WAX/RP resins as described in the art. Samples were eluted twice, each in 200 μL of acetonitrile with 1% formic acid. Prior to LC-MS/MS analysis, samples were dried down to 100 μL and diluted with 700 μL of water.

Ultra-performance liquid chromatography was performed on a Thermo-Scientific™ Vanquish™ UHPLC system using a Phenomenex Kinetex® Phenyl-Hexyl 100 Å column (4.6×50 mm, 2.6 μm). The column was heated to 40° C. with a gradient elution with a flow rate of 0.6 mL/min. Mobile phase A consisted of 0.1% formic acid in ultrapure water and mobile phase B consisted of 0.1% formic acid in acetonitrile. The system was equilibrated in 95% A for the first 0.5 minutes and the gradient consisted of 5-95% B from 0.5-3.0 minutes and re-equilibrated at initial conditions from 4.0-6.0 minutes. The liquid chromatography (LC) system was connected to a Thermo-Scientific™ Endura™ Triple Quadrupole mass spectrometer with an electrospray ionization source, operated in positive mode. Detection was performed by multiple reaction monitoring (MRM) analysis of the most intense transitions originating from the protonated molecular ion [M+1] of each analyte. The aglycone species was quantified and enzyme activity was expressed as pmol·min⁻¹·mg⁻¹.

Based on the enzyme activity results across the pH and temperature ranges, activity surface plots were prepared. FIG. 4 shows the activity surface plots for BpGUS on the fourteen different glucuronidated drug substrates tested across the indicated pH and temperature ranges. FIG. 5 shows the activity surface plots for EeGUS on the fourteen different glucuronidated drug substrates tested across the indicated pH and temperature ranges. FIG. 6 shows the activity surface plots for the chimeric BGUS enzyme Rxn3 (which has the amino acid sequence shown in SEQ ID NO: 19) on the fourteen different glucuronidated drug substrates tested across the indicated pH and temperature ranges. The data from these activity surface plots can be used to optimize enzyme blends for particular conditions, as described further in Example 5.

Example 5: Optimizing an Enzyme Blend for a Particular Condition

In this example, two approaches are described for optimizing an enzyme blend for a particular set of conditions, such as pH and temperature.

The pH and temperature profiles for each enzyme under consideration for inclusion in the blend must be determined (e.g., as described in Examples 3 and 4) or reasonably predicted, and then combined to create an activity surface (as described in Example 4 and shown in FIGS. 4 and 5 ). There are two ways this can be done. First, except for amplitude, activity profiles are often more dependent upon the enzyme than any particular substrate. Therefore, in theory any substrate may be used to determine the shape of the activity surface for an enzyme, and it may not be necessary to measure the entire activity surface for every substrate; once the shape is determined, it may be multiplied by the relative amplitude of each individual substrate to determine the activity surface for that substrate. Thus, one substrate can be used to determine the topology of the pH versus temperature (T) activity landscape, preferably an easily assayed substrate such as phenolphthalein-β-D-glucuronide. Secondly, however, if possible, measuring the activity surface for each individual enzyme with every target substrate of interest is preferred and can be determined using the methodology described in Example 4.

Furthermore, if desired or necessary, both temperature and pH profiles can be mathematically modeled using formulae known in the art. For example, enzyme activity increases exponentially with increasing temperature (the Q₁₀ law, or temperature coefficient), up until it reaches the temperature at which the rate of denaturation overwhelms the catalytic rate. Similarly, the pH profile can be modeled using titration curves that reflect the pK_(a)s of the two active-site residues (McIntosh et al. (1996) Biochemistry 35:9958; Joshi et al. (2001) Biochemistry 40:10115). An activity surface based either on comprehensive measurements for a model substrate (such as PTGlcU) or a surface based on modeling is normalized by dividing all activities by the activity at the optimal pH and preferred temperature.

The activity of each individual enzyme against a panel of substrates is determined at the enzyme's optimal pH and the preferred assay temperature. Multiplying these values over the pH and temperature profiles (either actual or modeled) creates activity surfaces for each enzyme-substrate pair.

The first model (referred to herein as Model 1) for optimizing enzyme blends involves maximizing the sum of the blended activity. In this model, to determine the activity of a blend at a given pH (pH_(m)) and temperature (T_(n)), the fractional activities for all enzymes for a particular substrate are summed. For the case where these activities have been measured directly for a given enzyme, Equation 1a (below) describes how this is done. For the case where activities are adjusted based on either a model substrate, where it has been measured for the whole activity surface, or where the activity surface itself has been modeled mathematically from a single point, the summed activities are calculated with Equation 1b (below). The product of summed activities for all substrates, theta (Θ), is then calculated with Equation 2 (below). The x^(th) root of theta is called the root sum activity. Using optimization software, the optimal blend for a particular pH and T is determined by maximizing theta, which represents the activity across all substrates at pH_(m) and T_(n). To determine the optimal blend for a wider pH and/or temperature range, the activity products for each point on the reaction surface are averaged and the fractional activities optimized by maximizing the average activity.

$\begin{matrix} {A_{1},{pH_{m}},{T_{n} = {\left( {{f_{a} \cdot A_{a}},{pH_{m}},T_{n}} \right) + \left( {{f_{b} \cdot A_{b}},{pH_{m}},T_{n}} \right) + \ldots\mspace{14mu} + \left( {{f_{z} \cdot A_{z}},{pH_{m}},T_{n}} \right)}}} & \underset{\_}{{Equation}\mspace{14mu} 1a} \\ {{{{A_{1},{pH_{m}},{T_{n} =}}\quad}\left( {{f_{a} \cdot A_{a} \cdot {\overset{\hat{}}{A}}_{a,{PTGlcU}}},{pH_{m}},T_{n}} \right)} + \left( {{f_{b} \cdot A_{b} \cdot {\overset{\hat{}}{A}}_{b,{PTGlcU}}},{pH_{m}},T_{n}} \right) + \ldots\mspace{14mu} + \left( {{f_{z} \cdot A_{z} \cdot {\overset{\hat{}}{A}}_{z,{PTGlcU}}},{pH_{m}},T_{n}} \right)} & \underset{\_}{{Equation}\mspace{14mu} 1b} \\ {{\Theta\; p\; H_{m}},{T_{n} = {\left( {A_{1},{pH_{m}},T_{n}} \right) \cdot {\quad{{\left( {A_{2},{pH_{m}},T_{n}} \right) \cdot \left( {A_{3},{pH_{m}},T_{n}} \right)}\ldots\mspace{14mu}\left( {A_{x},{pH_{m}},T_{n}} \right)}}}}} & \underset{\_}{{Equation}\mspace{14mu} 2} \end{matrix}$ Where:

-   -   A=activity, typically expressed in moles product per gram         protein per unit time (for example: pmol·mg⁻¹·min⁻¹), for the         enzyme, substrate, and/or condition indicated by the subscripts;     -   Â=normalized activity based on the indicated model substrate         and/or activity surface model. For this example, PTGlcU is the         model substrate. However, the ordinarily skilled artisan will         appreciate that other model substrates can be used, and this         example is not limited to PTGlcU and does not exclude the         possibility of using other model substrates;     -   a, b, . . . z=different enzymes;     -   1, 2, . . . x=different substrates;     -   m and n denote particular pH and temperature, respectively;     -   f=fraction of the subscripted enzyme in a blend. f_(a)+f_(b)+ .         . . +f_(z)=1; 0≤f≤1 for all f.

A blend may be further optimized for a contiguous range of pH and temperature by averaging theta across the range and adjusting the fractions to maximize the average.

The second model (referred to herein as Model 2) for optimizing enzyme blends utilizes the minimal enzyme amounts required for complete hydrolysis at specified conditions (pH and temperature for a defined length of time) and thus is also referred to as a minimal enzyme requirement model. In this model, to determine an amount of enzyme blend and a blend ratio to completely hydrolyze all fourteen substrates at a given pH (pH_(m)) and temperature (T_(n)), the total amount of blended enzyme to achieve a complete hydrolysis of each substrate at a given blend ratio and at a given time (t) is solved using Equation 3. To determine the optimal blend for minimal enzyme amount requirement at a given pH and temperature, the blend ratio is solved by minimizing the total amount of blended enzyme required to complete hydrolysis for all fourteen substrates within a given incubation time.

$\begin{matrix} {{{{\lambda_{1}pH_{m}},{T_{n} =}}\quad}\left( \frac{{pmol}_{1}}{\begin{matrix} \left( {\left( {{f_{a} \cdot A_{a}},{p\; H_{m}},T_{n}} \right) + \left( {{f_{b} \cdot A_{b}},{pH_{m}},T_{n}} \right) + \ldots\mspace{14mu} +} \right. \\ {\left. \left( {{f_{z} \cdot A_{z}},{p\; H_{m}},T_{n}} \right) \right) \cdot t} \end{matrix}} \right)} & {{Equation}\mspace{14mu} 3} \end{matrix}$ Where

-   -   λ=total amount of blended enzyme expressed in milligrams;     -   pmol=total amount of substrate expressed in picomoles;     -   a, b, . . . z=different enzymes;     -   1, 2, . . . x=different substrates; m and n denote particular pH         and temperature, respectively;     -   f=fraction of the subscripted enzyme in a blend. f_(a)+f_(b)+ .         . . +f_(z)=1; 0≤f≤1 for all f,     -   t=incubation time desired to complete hydrolysis expressed in         minutes.

Example 6: Enzyme Blends Exhibit Synergistic Activity

In this example, the optimization approach described in Example 5 was used to determine optimized blends of BpGUS and EeGUS (referred to herein as BpGUS/EeGUS) and of EeGUS and the chimeric enzyme Rxn3 (referred to herein as EeGUS/Rxn3) for optimized activity against the fourteen substrates described in Example 4. The blends were then tested experimentally and shown to have not merely additive activity against the substrates, but rather exhibited synergistic activity against the substrates.

For the BpGUS/EeGUS blend, first the surface activity profiles described in Example 4, and shown in FIGS. 4 and 5 for BpGUS and EeGUS, respectively, were applied to the maximizing sum of blended activity (Model 1) optimization approach described in Example 5. This provided a theoretical expected summed activity level for preparations containing 100% BpGUS, 86%/14% BpGUS/EeGUS, 75%/25% BpGUS/EeGUS, 50%/50% BpGUS/EeGUS, 25%/75% BpBUS/EeGUS and 100% EeGUS.

Blends of BpGUS and EeGUS containing the above-listed fractions of each enzyme were then prepared and their enzymatic activity against the fourteen substrates described in Example 4 was tested experimentally at pH 5.5 and 20° C. Each beta-glucuronidase blended formulation was used to deconjugate fourteen glucuronidated drugs frequently tested in urine drug testing applications. The substrates represent a wide variety of drug classes, such as opiates, synthetic opioids, benzodiazepines, and anti-depressants. The substrates included morphine-3-β-D-glucuronide, oxymorphone-3-β-D-glucuronide, hydromorphone-3-β-D-glucuronide, codeine-6-β-D-glucuronide, dihydrocodeine-6-β-D-glucuronide, buprenorphine-3-β-D-glucuronide, norbuprenorphine-3-β-D-glucuronide, tapentadol glucuronide, O-desmethyltramadol glucuronide, O-desmethylvenlafaxine glucuronide, amitriptyline-N-β-D-glucuronide, lorazepam glucuronide, oxazepam glucuronide and temazepam glucuronide. The substrates were fortified in synthetic urine at a concentration equivalent to 500 ng/mL when liberated.

Blended formulations were prepared from 2 or more β-glucuronidase solutions at 2 mg/mL, combined in different v/v percentages. The hydrolysis buffer used was 0.2 M sodium acetate, pH 5.5. The internal standard solution was prepared at 1 μg/mL of each deuterated drug standard in methanol. 50 μL of urine containing the fourteen substrates was mixed with 150 μL of hydrolysis buffer, 20 μL enzyme blend solution (five different dilutions) and 10 μL internal standard solution. The incubation was performed at 20° C. for 15 minutes. Samples were extracted using dispersive pipette extraction tips with WAX/RP resins as described in the art. Samples were eluted twice, each in 200 μL of acetonitrile with 1% formic acid. Prior to LC-MS/MS analysis, samples were dried down to 100 μL and diluted with 700 μL water.

The same LC-MS/MS methodology described in Example 4 was used. The aglycone species was quantified and enzyme activity was expressed as pmol·min⁻¹·mg⁻¹

The blended root sum activity (the x^(th) root of theta; pmol/ming mg protein) for each BpGUS/EeGUS blend was determined for the experimental results and compared to the theoretical expected values. This comparison is shown in the bar graph of FIG. 7 . These results demonstrated that the experimentally-determined levels of blended root sum activity were actually higher than the theoretical expected values (which were based on an additive effect), thereby demonstrating that the enzyme blends were exhibiting synergistic activity against the substrates, not merely an additive effect. In particular, the optimized blend containing 25%/75% BpGUS/EeGUS exhibited significantly higher experimentally-determined blended root sum activity than the theoretical expected values, thereby demonstrating synergistic activity of BpGUS and EeGUS against the fourteen tested substrates when blended together at the indicated fractions.

Similarly, for the EeGUS/Rxn3 blend, first the surface activity profiles described in Example 4, and shown in FIGS. 5 and 6 for EeGUS and Rxn3, respectively, were applied to the maximizing sum of blended activity (Model 1) optimization approach described in Example 5. This provided a theoretical expected summed activity level for preparations containing 100% EeGUS, 70%/30% EeGUS/Rxn3, 50%/50% EeGUS/Rxn3, 30%/70% EeGUS/Rxn3 and 100% Rxn3.

Blends of EeGUS and Rxn3 containing the above-listed fractions of each enzyme were then prepared and their enzymatic activity against the fourteen substrates described in Example 4 was tested experimentally at 20° C. over pH ranging from 4.0 to 7.0, as described above for the BpGUS/EeGUS blends. Surface activity profiles (determined as described in Example 4) for the EeGUS/Rxn3 blends against the fourteen substrates at 20° C. over pH 4.0-7.0 as a function of the percentage of EeGUS in the EeGUS/Rxn3 blend are shown in FIG. 8 .

The blended root sum activity (the x^(th) root of theta; pmol/min·mg protein) for each EeGUS/Rxn3 blend was determined for the experimental results and compared to the theoretical expected values. This comparison is shown in the bar graph of FIG. 9 . These results demonstrated that the experimentally-determined levels of blended root sum activity were actually higher than the theoretical expected values (which were based on an additive effect) for certain blend percentages, thereby demonstrating that the enzyme blends were exhibiting synergistic activity against the substrates, not merely an additive effect. In particular, the optimized blend containing 70%/30% EeGUS/Rxn3 exhibited higher experimentally-determined blended root sum activity than the theoretical expected values, thereby demonstrating synergistic activity of EeGUS and Rxn3 against the fourteen tested substrates when blended together at the indicated fractions.

Example 7: Comparison of Blend Optimization Approaches

In this example, the two optimization approaches described in Example 5, referred to as Model 1 and Model 2, were compared for BpGUS/EeGUS and EeGUS/Rxn3 blends.

For the BpGUS/EeGUS blends, the percentage of BpGUS in the blend needed to achieve maximal root sum activity (calculated by Model 1) is shown in FIG. 10A across range of temperatures (20° C. to 40° C.) and a range of pHs (pH 4 to 7). The percentage of BpGUS in the blend needed to achieve the minimal enzyme amount required for complete hydrolysis for a 15 minute incubation (calculated by Model 2) is shown in FIG. 10B across range of temperatures (20° C. to 40° C.) and a range of pHs (pH 4 to 7).

For the EeGUS/Rxn3 blends, the percentage of Rxn3 in the blend needed to achieve maximal root sum activity (calculated by Model 1) is shown in FIG. 11A across range of temperatures (20° C. to 40° C.) and a range of pHs (pH 4 to 7). The percentage of Rxn3 in the blend needed to achieve the minimal enzyme amount required for complete hydrolysis for a 15 minute incubation (calculated by Model 2) is shown in FIG. 11B across range of temperatures (20° C. to 40° C.) and a range of pHs (pH 4 to 7).

EQUIVALENTS

Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents of the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the following claims.

SUMMARY OF SEQUENCE LISTING SEQ ID NO: DESCRIPTION 1 MLKPQQTTTRDLISLDGLWKFALAS DDNNTQPWTSQLKTSLECPVPASYN DIFADSKIHDHVGWVYYQRDVIVPK GWSEERYLVRCEAATHHGRIYVNGN LVADHVGGYTPFEADITDLVAAGEQ FRLTIAVDNELTYQTIPPGKVEILE ATGKKVQTYQHDFYNYAGLARSVWL YSVPQQHIQDITVRTDVQGTTGLID YNVVASTTQGTIQVAVIDEDGTTVA TSSGSNGTIHIPSVHLWQPGAAYLY QLHASIIDSSKKTIDTYKLATGIRT VKVQGTQFLINDKPFYFTGFGKHED TNIRGKGHDDAYMVHDFQLLHWMGA NSFRTSHYPYAEEVMEYADRQGIVV IDETPAVGLAFSIGAGAQTSNPPAT FSPDRINNKTREAHAQAIRELIHRD KNHPSVVMWSIANEPASNEDGAREY FAPLPKLARQLDPTRPVTFANVGLA TYKADRIADLFDVLCLNRYFGWYTQ TAELDEAEAALEEELRGWTEKYDKP IVMTEYGADTVAGLHSVMVTPWSEE FQVEMLDMYHRVFDRFEAMAGEQVW NFADFQTAVGVSRVDGNKKGVFTRD RKPKAAAHLLRKRWTNLHNGTAEGS KTFQ (AoGUS: Aspergillus oryzae BGUS) 2 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGFGKH EDSAVRGKGYDPAYMVHDFQLMDWM GANSFRTSHYPYAEEVMEFADRHGI VVIDETPAVGLAFSIGSGVSSEDSP QTFTPEGINNNTREAHKQAIRELIA RDKNHASVVMWSIANEPASQEVGAR EYFAPLVDLAHELDPSRPVCFANYG DATYEVDRISDMEDVLCLNRYFGWY SQTGEVEEAEAALEKELLGWEGKYG KPIVITEYGADTMAGLHSVLALPWS EEFQVQLLDMYHRVFDRIDSVVGEH VWNFADFQTAVGIIRVDGNKKGVFT RERKPKAAAHTLKTRWSGMLGSDH  (AtGUS: Aspergillus terreus BGUS) 3 MKKLLAAAMLFMLNSWSCFSADTPR AEYPRPQFEREQWVNLNGTWTFDFD FGKSGKDRRLQSAEKEDKNITVPFC PESKLSGVGYTDFIEQMWYQRNITI PSDWNGKKIFLNEGAVDYCAEIYVD GKFVQRHFGGSSSFAVDLTRYVTPG KTHNLVVFVQDDLRSGLQTGGKQCG NYYSGGCSYTRTTGIWQTVWMEAVS ADGLKSVFVRPDIDQKQLVIEPEFY NESANTLEITLKDRNKTVAKKSVNC ANSSVVVLPVKNMKLWSPEDPFLYD LVYQVKDAKGNVLDEVKSYAGMRKV HTANGRFYLNNQPYFQRLVLDQGFY PEGIWTAPSDEDLKNDIVLGKEAGF NGARLHQKVFEERYYYWADKLGYIT WGESASWMLDVNKELAARNFLGEWS EVVVRDRNHPSLVTWTPFNETWGGG PDAYIRLVRDVYNITKAIDPTRPVN DASGDNHVITDIWSVHNYEQDRAKL TEQLKMEEGKEPYRNARDKDFLAVY EGQPYMVDEFGGIPWMAEKDRKNSW GYGGMPENAEAFYKRLEGQIDAFID SPHVTGFCYTQLTDVEQEKNGIYYY DRTPKLDMKRIKAIFEKIK (BfGUS: Bacteroides fragilis BGUS) 4 MKTLLKNSLTFLLMLMPVLAFAQQA PQIMNVSARQTTSLDGQWKTIVDPF ENGYYDYRLKPYDGGYAQDKTYSDK TKLQEYDFETDKLLFVPGDWNTQRP QLYYYEGTVWYRKHFEYSLQPGKRL FLNEGAVNYEAIVWLNGKRLGRHIG GFTPFNFEITNLLKEGTNSLVVKVD NKRLPEAVPTVNADWWNFGGITRPV TLIEMPATYIRDYYVQLAKDDKNMI EGWVQLEGSDKEQKITLDIPELKVK KEVTTDANGYASFLIKSKPILWTPE NPKLYAVNLASETDKVSDEIGERTI RTEGIKILLNDKEIFCRGISIHEET PYYSGRAYSKDHAHTLLSWAKELGC NEVRLAHYPHNEEMVREAERMGELV WSEIPVYWTIHWENKDTYQNAEQQL CDMIARDKNRCNIIIWSIANETPHS ETRLTELSNLANKARSLDSVRLIGA AMEKEEVQPGVLTVNDPLGELLDII SFNEYVGWYDGDSEKCDRVNWTEDT QKPVFISELGGGALYGRHGSPKERF TEEYQEDLYIRHVNMLKRIPGLAGT TPWILKDERSPRRHVPEIQDDENRK GLVSDKGQKKKAFFVLQKWYKELTE AYK (BuGUS: Bacteroides uniformis BGUS) 5 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpGUS: Brachyspira pilosicoli BGUS) 6 MVNSMLYPRESRTRRVVDISGMWEF KIDSNNEGRKNGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDIWYETS FYLPLEWKDKNVNIRFGCATHEAAV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTLPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIYDIDILSDINGSD GIVNYEVHTTGENKVEVKIYDEEGK EAASAEGKNGKIVIKNAKLWNPKAA YLYKFEACIKNGEELIDEYYLDEGI RTIKVEGTKFLINGKPFYFTGFGKH EDSETAGRGYNPPVIKRDFELIKWI GANSFRTSHYPYSEEIMQAADREGI VIIDEIAAVGMFDVGSVLNPGASKA DYFSLEEVHTKTKEIHKKAVEELIT RDKNHPSVVMWSLENEPDTSKDEAL PYFEDIENFAKSIDKQNLPKTFAAI QASAPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYKDM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIIGE QLWNFADFQTTEGIFRVDGNKKGIF TRTRQPKAVAHYIRSRWTKLPLDYK K (BmGUS: Brachyspira murdochii BGUS) 7 MLYPIITESRQLIDLSGIWKFKLNE GNGLTEELSKAPLEDTIEMAVPSSY NDLVESQEVRDHVGWVWYERNFTIP KTLLNERIVLREGSATHEAKVYLNG ELLVEHKGGFTPFEAEINDLLVSGD NRLTVAVNNIIDETTLPVGLVKEVE VDGKKVIKNSVNEDFFNYAGIHRPV KIYTTPKSYIEDITIVTDEKENNGY VNYEVQAVGKCNIKVTIIDEENNIV AEGEGKEGKLTINNVHLWEPMNAYL YKLKVELLDDEEIIDTYFEEFGVRT VEVKDGKFLINNKPFYFKGFGKHED SYVNGRGINEAINIKDFNLMKWIGA NSFRTSHYPYSEEIMRLADREGIVV IDETPAVGLHLNEMATGEGGDAPKR DTWKEIGTKEAHERILRELVSRDKN HPCVVMWSVANEPDSDSEGAKEYFE PLIKLTKELDPQKRPVTVVTYLMST PDRCKVGDIVDVLCLNRYYGWYVAG GDLEEAKRMLEDELKGWEERCPKTP IMFTEYGADTVAGLHDTVPVMFTEE YQVEYYKANHEVMDKCKNEVGEQVW NFADFATSQGIIRVQGNKKGIFTRE RKPKMIAHSLRERWTNIPEFGYKK (CpGUS: Clostridium perfringens BGUS) 8 MLRPVETPTREIKKLDGLWAFSLDR ENCGIDQRWWESALQESRAIAVPGS ENDQFADADIRNYAGNVWYQREVFI PKGWAGQRIVLRFDAVTHYGKVWVN NQEVMEHQGGYTPFEADVTPYVIAG KSVRITVCVNNELNWQTIPPGMVIT DENGKKKQSYFHDFFNYAGIHRSVM LYTTPNTWVDDITVVTHVAQDCNHA SVDWQVVANGDVSVELRDADQQVVA TGQGTSGTLQVVNPHLWQPGEGYLY ELCVTAKSQTECDIYPLRVGIRSVA VKGEQFLINHKPFYFTGEGRHEDAD LRGKGEDNVLMVHDHALMDWIGANS YRTSHYPYAEEMLDWADEHGIVVID ETAAVGENLSLGIGFEAGNKPKELY SEEAVNGETQQAHLQAIKELIARDK NHPSVVMWSIANEPDTRPQGAREYE APLAEATRKLDPTRPITCVNVMECD AHTDTISDLFDVLCLNRYYGWYVQS GDLETAEKVLEKELLAWQEKLHQPI IITEYGVDTLAGLHSMYTDMWSEEY QCAWLDMYHRVEDRVSAVVGEQVWN FADFATSQGILRVGGNKKGIFTRDR KPKSAAFLLQKRWTGMNFGEKPQQG GKQ (EcGUS: Escherichia coli BGUS) 9 MLRPVETPTREIKKLDGLWAFSLDR ENCGIDQRWWESALQESRAIAVPGS ENDQFADADIRNYAGNVWYQREVFI PKGWAGQRIVLRFDAVTHYGKVWVN NQEVMEHQGGYTPFEADVTPYVIAG KSVRITVCVNNELNWQTIPPGMVIT DENGKKKQSYFHDFFNYAGIHRSVM LYTTPNTWVDDITVVTHVAQDCNHA SVDWQVVANGDVSVELRDADQQVVA TGQGTSGTLQVVNPHLWQPGEGYLY ELCVTAKSQTECDIYPLRVGIRSVA VKGEQFLINHKPFYFTGEGRHEDAD LRGKGEDNVLMVHDHALMDWIGANS YRTSHYPYAEEMLDWADEHGIVVID ETAAVGENLSLGIGFEAGNKPKELY SEEAVNGETQQAHLQAIKELIARDK NHPSVVMWSIANEPDTRPQGAREYE APLAEATRKLDPTRPITCVNVMECD AHTDTISDLFDVLCLNRYYGWYVQS GDLETAEKVLEKELLAWQEKLHQPI IITEYGVDTLAGLHSMYTDMWSEEY QCAWLDMYHRVEDRVSAVVGEQVWN FADFATSQSILRVGGNKKGIFTRDR KPKSAAFLLQKRWTGMNFGEKPQQG SKQGLCGR (EcE1F: IMCSzyme® variant Escherichia coli K12 BGUS) 10 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeGUS: Eubacterium eligens BGUS) 11 MARGSAVAWAALGPLLWGCALGLQG GMLYPQESPSRECKELDGLWSFRAD FSDNRRRGFEEQWYRRPLWESGPTV DMPVPSSENDISQDWRLRHEVGWVW YEREVILPERWTQDLRTRVVLRIGS AHSYAIVWVNGVDTLEHEGGYLPFE ADISNLVQVGPLPSRLRITIAINNT LTPTTLPPGTIQYLTDTSKYPKGYF VQNTYFDFFNYAGLQRSVLLYTTPT TYIDDITVTTSVEQDSGLVNYQISV KGSNLFKLEVRLLDAENKVVANGTG TQGQLKVPGVSLWWPYLMHERPAYL YSLEVQLTAQTSLGPVSDFYTLPVG IRTVAVTKSQFLINGKPFYFHGVNK HEDADIRGKGEDWPLLVKDFNLLRW LGANAFRTSHYPYAEEVMQMCDRYG IVVIDECPGVGLALPQFFNNVSLHH HMQVMEEVVRRDKNHPAVVMWSVAN EPASHLESAGYYLKMVIAHTKSLDP SRPVTFVSNSNYAADKGAPYVDVIC LNSYYSWYHDYGHLELIQLQLATQF ENWYKKYQKPIIQSEYGAETIAGEH QDPPLMFTEEYQKSLLEQYHLGLDQ KRRKYVVGELIWNFADFMTEQSPTR VLGNKKGIFTRQRQPKSAAFILRER YWKIANETRYPHSVAKSQCLENSLF T (HsGUS: Homo sapiens BGUS) 12 MLYPMETASRVVLDLSGVWREMIDK EQIPVDVTRPLPATLSMAVPASEND QTASKEIREHVGYVWYERCFELPQL LRQERLVLREGSATHEAWVYLNGHL ITHHKGGFTPFEVEINDDLVTGENR LTVKLSNMLDYTTLPVGHYKETQNE TGQRVRQLDENEDFFNYAGLQRPVK IYSTPHSYIRDITLTPKVNLTNHSA VVNGEIETVGDVEQVVVTILDEDNQ VVGTTSGKTLAIELNSVHLWQPGKA YLYRAKVELYQAGQVIDTYIETEGI RQIAVKAGKFLINGQPFYFKGFGKH EDAYIHGRGLSEPQNVLDLSLMKQM GANSFRTSHYPYSEEMMRLCDREGI VVIDEVPAVGLMLSETEDVSALEKD DFEDDTWEKLRTAEAHRQAITEMID RDKNHASVVMWSISNEAANFSKGAY EYEKPLFDLARKLDPQQRPCTYTSI MMTTLKTDRCLALADVIALNRYYGW YMGNGDLKAAETATREELLAYQAKE PDKPIMYTEYGADTIAGLHSNYDEP FSEEFQEDYYRMCSRVEDEVTNEVG EQLWNFADFQTKEGIQRVQGNKKGI FTRAREPKMVVRYLTQRWRNIPDFN YKK (LbLR2D: Lactobacillus brevis BGUS) 13 MSLKWSACWVALGQLLCSCALALKG GMLFPKESPSRELKALDGLWHFRAD LSNNRLQGFEQQWYRQPLRESGPVL DMPVPSSENDITQEAALRDFIGWVW YEREAILPRRWTQDTDMRVVLRINS AHYYAVVWVNGIHVVEHEGGHLPFE ADISKLVQSGPLTTCRITIAINNTL TPHTLPPGTIVYKTDTSMYPKGYFV QDTSFDFFNYAGLHRSVVLYTTPTT YIDDITVITNVEQDIGLVTYWISVQ GSEHFQLEVQLLDEGGKVVAHGTGN QGQLQVPSANLWWPYLMHEHPAYMY SLEVKVTTTESVTDYYTLPIGIRTV AVTKSKFLINGKPFYFQGVNKHEDS DIRGKGEDWPLLVKDFNLLRWLGAN SFRTSHYPYSEEVLQLCDRYGIVVI DECPGVGIVLPQSEGNESLRHHLEV MEELVRRDKNHPAVVMWSVANEPSS ALKPAAYYFKTLITHTKALDLTRPV TFVSNAKYDADLGAPYVDVICVNSY FSWYHDYGHLEVIQPQLNSQFENWY KTHQKPIIQSEYGADAIPGIHEDPP RMFSEEYQKAVLENYHSVLDQKRKE YVVGELIWNFADFMTNQSPLRVIGN KKGIFTRQRQPKTSAFILRERYWRI ANETGGHGSGPRTQCFGSRPFTF (Mus musculus BGUS) 14 MKRISIAELSLELCVASVWSMPRPE YPRPQFERAGWVNLNGEWTCSFDEG GSGMEREFYKSKGEDKKITVPFCPE SKLSGIGYTDFINHEWYQRPITIPQ EWNGKNILLNEGAVYYKSEVYIDGV LASRHFGGTSSFAVDITSLVKPGQT HSLVVYVESDVRGAKQAAGKQNLQY ASYGCNYTRTTGIWQTVWMEAVHPE GLQSIQLLTDIDQQQLVVRPREYKE AGGKLQVTLKDNGKVVASRTVSASS LSSVVLPVKKMKTWSPESPFLYDLE YKVLDKNGNIIDEVNGYAGMRKVHI EGNKIYLNNKPYYQRLVLDQGFYPD GIWTAPSDEALKRDIELSMEAGFNG ARLHQKVFEERFYYWADKMGYLTWG EASSWGMDCNDTETARNFITEW SEIVQRDRNHPSLLIWTPTNEE FWPDRVQYPRLMHDLYNLTKMIDPT RPFHGASGGTHIATDIWTVHNYEQD PAKLKEKLYNGGKLMEAPKWEIHLM PMNIGYNGLKYTDQYAFPEYKKDMP YLVDEFGGIKWNPSQQMESAQNTSW GYGEPPRSLEEFYARLEGQVDAVLS LSNDIWGYCYTQLTDVEQEQNGIYY YDRTPKFDMKRIHAIFSKTPESK (PmGUS: Parabacteroides sp. merdae BGUS) 15 MLYPINTETRGVEDLNGVWNFKLDY GKGLEEKWYESKLTDTISMAVPSSY NDIGVTKEIRNHIGYVWYEREFTVP AYLKDQRIVLRFGSATHKAIVYVNG ELVVEHKGGFLPFEAEINNSLRDGM NRVTVAVDNILDDSTLPVGLYSERH EEGLGKVIRNKPNEDFFNYAGLHRP VKIYTTPFTYVEDISVVTDFNGPTG TVTYTVDFQGKAETVKVSVVDEEGK VVASTEGLSGNVEIPNVILWEPLNT YLYQIKVELVNDGLTIDVYEEPFGV RTVEVNDGKFLINNKPFYFKGFGKH EDTPINGRGENEASNVMDFNILKWI GANSFRTAHYPYSEELMRLADREGL VVIDETPAVGVHLNEMATTGLGEGS ERVSTWEKIRTFEHHQDVLRELVSR DKNHPSVVMWSIANEAATEEEGAYE YEKPLVELTKELDPQKRPVTIVLEV MATPETDKVAELIDVIALNRYNGWY EDGGDLEAAKVHLRQEFHAWNKRCP GKPIMITEYGADTVAGFHDIDPVMF TEEYQVEYYQANHVVEDEFENFVGE QAWNFADFATSQGVMRVQGNKKGVF TRDRKPKLAAHVERERWTNIPDEGY KN (StpGUS: Staphylococcus sp. RLH1 BGUS) 16 MLYPLLTKTRNTYDLGGIWNFKLGE HNPNELLPSDEVMVIPTSENDLMVS KEKRDYIGDFWYEKVIEVPKVSEGE EMVLREGSVTHQAKIYVDGILVGEH KGGFTPFEVLVPECKYNNEKIKVSI CANNVLDYTTLPVGNYSEIIQEDGS IKKKVRENFDFFNYAGVHRPLKLMI RPKNHISDITITSRLSDDLQSADLH ELVETNQKVDEVRISVEDEDNKLVG ETKDSRLFLSDVHLWEVLNAYLYTA RVEIFVDNQLQDVYEENEGLREIEV TNGQFLLNRKPIYFKGFGKHEDTFI NGRGLNEAANLMDLNLLKDIGANSF RTSHYPYSEEMMRLADRMGVLVIDE VPAVGLFQNFNASLDLSPKDNGTWS LMQTKAAHEQAIQELVKRDKNHPSV VMWVVANEPASHEAGAHDYFEPLVK LYKDLDPQKRPVTLVNILMATPDRD QVMDLVDVVCLNRYYGWYVDHGDLT NAEVGLRKELLEWQDKEPDKPIIIT EYGADTLPGLHSTWNIPYTEEFQCD FYEMSHRVEDGIPNLVGEQVWNFAD FETNLMILRVQGNHKGLFSRNRQPK QVVKEFKKRWMTIPHYHNKKNSVK (SaGUS: Streptococcus agalactiae BGUS) 17 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGFGKH EDTNIRGKGHDDAYMVHDFQLLHWM GANSFRTSHYPYAEEVMEYADRQGI VVIDETPAVGLAFSIGAGAQTSNPP ATFSPDRINNKTREAHAQAIRELIH RDKNHPSVVMWSIANEPASNEDGAR EYFAPLPKLARQLDPTRPVTFANVG LATYKADRIADLFDVLCLNRYFGWY TQTAELDEAEAALEEELRGWTEKYD KPIVMTEYGADTVAGLHSVMVTPWS EEFQVEMLDMYHRVFDRFEAMAGEQ VWNFADFQTAVGVSRVDGNKKGVFT RDRKPKAAAHLLRKRWTNLHNGTAE GSKTFQ (Rxn1 chimera) 18 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGEGRH EDADLRGKGEDNVLMVHDHALMDWI GANSYRTSHYPYAEEMLDWADEHGI VVIDETAAVGENLSLGIGFEAGNKP KELYSEEAVNGETQQAHLQAIKELI ARDKNHPSVVMWSIANEPDTRPQGA REYEAPLAEATRKLDPTRPITCVNV MECDAHTDTISDLFDVLCLNRYYGW YVQSGDLETAEKVLEKELLAWQEKL HQPIIITEYGVDTLAGLHSMYTDMW SEEYQCAWLDMYHRVFDRVSAVVGE QVWNFADFATSQSILRVGGNKKGIF TRDRKPKSAAFLLQKRWTGMNFGEK PQQGSKTFQ (Rxn2 chimera) 19 MLKPQQTTTRDLISLDGLWKFALAS DDNNTQPWTSQLKTSLECPVPASYN DIFADSKIHDHVGWVYYQRDVIVPK GWSEERYLVRCEAATHHGRIYVNGN LVADHVGGYTPFEADITDLVAAGEQ FRLTIAVDNELTYQTIPPGKVEILE ATGKKVQTYQHDFYNYAGLARSVWL YSVPQQHIQDITVRTDVQGTTGLID YNVVASTTQGTIQVAVIDEDGTTVA TSSGSNGTIHIPSVHLWQPGAAYLY QLHASIIDSSKKTIDTYKLATGIRT VKVQGTQFLINDKPFYFTGFGKHED SAVRGKGYDPAYMVHDFQLMDWMGA NSFRTSHYPYAEEVMEFADRHGIVV IDETPAVGLAFSIGSGVSSEDSPQT FTPEGINNNTREAHKQAIRELIARD KNHASVVMWSIANEPASQEVGAREY FAPLVDLAHELDPSRPVCFANYGDA TYEVDRISDMFDVLCLNRYFGWYSQ TGEVEEAEAALEKELLGWEGKYGKP IVITEYGADTMAGLHSVLALPWSEE FQVQLLDMYHRVEDRIDSVVGEHVW NFADFQTAVGIIRVDGNKKGVETRE RKPKAAAHTLKTRWSGMLGSDH (Rxn3 chimera) 20 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGEGRH EDADLRGKGEDNVLMVHDHALMDWI GANSYRTSHYPYAEEMLDWADEHGI VVIDETAAVGENLSLGIGFEAGNKP KELYSEEAVNGETQQAHLQAIKELI ARDKNHPSVVMWSIANEPDTRPQGA REYEAPLAEATRKLDPTRPITCVNV MECDAHTDTISDLEDVLCLNRYYGW YVQSGDLETAEKVLEKELLAWQEKL HQPIIITEYGVDTLAGLHSMYTDMW SEEYQCAWLDMYHRVEDRVSAVVGE QVWNFADFATSQSILRVGGNKKGIF TRDRKPKSAAFLLQKRWTGMNFGEK PQQGSDH (Rxn4 chimera) 21 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGEGRH EDADLRGKGEDNVLMVHDHALMDWI GANSYRTSHYPYAEEMLDWADEHGI VVIDETAAVGENLSLGIGFEAGNKP KELYSEEAVNGETQQAHLQAIKELI ARDKNHPSVVMWSIANEPDTRPQGA REYEAPLAEATRKLDPTRPITCVNV MECDAHTDTISDLEDVLCLNRYYGW YVQSGDLETAEKVLEKELLAWQEKL HQPIIITEYGVDTLAGLHSMYTDMW SEEYQCAWLDMYHRVEDRVSAVVGE QVWNFADFATSQSILRVGGNKKGIF TRDRKPKSAAFLLQKRWTGMNFGEK PQQGSKQGLCGR (Rxn5 chimera) 22 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGEGRH EDADLRGKGEDNVLMVHDHALMDWI GANSYRTSHYPYAEEMLDWADEHGI VVIDETAAVGENLSLGIGFEAGNKP KELYSEEAVNGETQQAHLQAIKELI ARDKNHPSVVMWSIANEPDTRPQGA REYEAPLAEATRKLDPTRPITCVNV MECDAHTDTISDLEDVLCLNRYYGW YVQSGDLETAEKVLEKELLAWQEKL HQPIIITEYGVDTLAGLHSMYTDMW SEEYQCAWLDMYHRVEDRVSAVVGE QVWNFADFATSQSILRVGGNKKGIF TRDRKPKSAAFLLQKRWTGMNFGEK PQQGSKQGLCGR (Rxn8 chimera) 23 MLRPVETPTREIKKLDGLWAFSLDR ENCGIDQRWWESALQESRAIAVPGS ENDQFADADIRNYAGNVWYQREVFI PKGWAGQRIVLRFDAVTHYGKVWVN NQEVMEHQGGYTPFEADVTPYVIAG KSVRITVCVNNELNWQTIPPGMVIT DENGKKKQSYFHDFFNYAGIHRSVM LYTTPNTWVDDITVVTHVAQDCNHA SVDWQVVANGDVSVELRDADQQVVA TGQGTSGTLQVVNPHLWQPGEGYLY ELCVTAKSQTECDIYPLRVGIRSVA VKGEQFLINHKPFYFTGFGKHEDTN IRGKGHDDAYMVHDFQLLHWMGANS FRTSHYPYAEEVMEYADRQGIVVID ETPAVGLAFSIGAGAQTSNPPATFS PDRINNKTREAHAQAIRELIHRDKN HPSVVMWSIANEPASNEDGAREYFA PLPKLARQLDPTRPVTFANVGLATY KADRIADLEDVLCLNRYFGWYTQTA ELDEAEAALEEELRGWTEKYDKPIV MTEYGADTVAGLHSVMVTPWSEEFQ VEMLDMYHRVFDRFEAMAGEQVWNF ADFQTAVGVSRVDGNKKGVFTRDRK PKAAAHLLRKRWTNLHNGTAEGSKQ GLCGR (Rxn9 chimera) 24 MLRPVETPTREIKKLDGLWAFSLDR ENCGIDQRWWESALQESRAIAVPGS ENDQFADADIRNYAGNVWYQREVFI PKGWAGQRIVLRFDAVTHYGKVWVN NQEVMEHQGGYTPFEADVTPYVIAG KSVRITVCVNNELNWQTIPPGMVIT DENGKKKQSYFHDFFNYAGIHRSVM LYTTPNTWVDDITVVTHVAQDCNHA SVDWQVVANGDVSVELRDADQQVVA TGQGTSGTLQVVNPHLWQPGEGYLY ELCVTAKSQTECDIYPLRVGIRSVA VKGEQFLINHKPFYFTGFGKHEDSA VRGKGYDPAYMVHDFQLMDWMGANS FRTSHYPYAEEVMEFADRHGIVVID ETPAVGLAFSIGSGVSSEDSPQTFT PEGINNNTREAHKQAIRELIARDKN HASVVMWSIANEPASQEVGAREYFA PLVDLAHELDPSRPVCFANYGDATY EVDRISDMEDVLCLNRYFGWYSQTG EVEEAEAALEKELLGWEGKYGKPIV ITEYGADTMAGLHSVLALPWSEEFQ VQLLDMYHRVEDRIDSVVGEHVWNF ADFQTAVGIIRVDGNKKGVFTRERK PKAAAHTLKTRWSGMLGSKQGLCGR (Rxn10 chimera) 25 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGFGKH EDSAVRGKGYDPAYMVHDFQLMDWM GANSFRTSHYPYAEEVMEFADRHGI VVIDETPAVGLAFSIGSGVSSEDSP QTFTPEGINNNTREAHKQAIRELIA RDKNHASVVMWSIANEPASNEDGAR EYFAPLPKLARQLDPTRPVTFANVG LATYKADRIADLEDVLCLNRYFGWY TQTAELDEAEAALEEELRGWTEKYD KPIVMTEYGADTVAGLHSVMVTPWS EEFQVEMLDMYHRVFDRFEAMAGEQ VWNFADFQTAVGVSRVDGNKKGVFT RDRKPKAAAHLLRKRWTNLHNGTAE GSKTFQ (Save1 chimera) 26 MLKPQQTTTRDLISLDGLWKFALAS DDNNTQPWTSQLKTSLECPVPASYN DIFADSKIHDHVGWVYYQRDVIVPK GWSEERYLVRCEAATHHGRIYVNGN LVADHVGGYTPFEADITDLVAAGEQ FRLTIAVDNELTYQTIPPGKVEILE ATGKKVQTYQHDFYNYAGLARSVWL YSVPQQHIQDITVRTDVQGTTGLID YNVVASTTQGTIQVAVIDEDGTTVA TSSGSNGTIHIPSVHLWQPGAAYLY QLHASIIDSSKKTIDTYKLATGIRT VKVQGTQFLINDKPFYFTGFGKHED TNIRGKGHDDAYMVHDFQLLHWMGA NSFRTSHYPYAEEVMEYADRQGIVV IDETPAVGLAFSIGAGAQTSNPPAT ESPDRINNKTREAHAQAIRELIHRD KNHPSVVMWSIANPASQEVGAREYF APLVDLAHELDPSRPVCFANYGDAT YEVDRISDMFDVLCLNRYFGWYSQT GEVEEAEAALEKELLGWEGKYGKPI VITEYGADTMAGLHSVLALPWSEEF QVQLLDMYHRVFDRIDSVVGEHVWN FADFQTAVGIIRVDGNKKGVFTRER KPKAAAHTLKTRWSGMLGSDH (Save3 chimera) 27 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGEGRH EDADLRGKGEDNVLMVHDHALMDWI GANSYRTSHYPYAEEMLDWADEHGI VVIDETPAVGLAFSIGSGVSSEDSP QTFTPEGINNNTREAHKQAIRELIA RDKNHASVVMWSIANEPDTRPQGAR EYEAPLAEATRKLDPTRPITCVNVM ECDAHTDTISDLEDVLCLNRYYGWY VQSGDLETAEKVLEKELLAWQEKLH QPIIITEYGVDTLAGLHSMYTDMWS EEYQCAWLDMYHRVEDRVSAVVGEQ VWNFADFATSQSILRVGGNKKGIFT RDRKPKSAAFLLQKRWTGMNFGEKP QQGSDH (Save4 chimera) 28 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGEGRH EDADLRGKGEDNVLMVHDHALMDWI GANSYRTSHYPYAEEMLDWADEHGI VVIDETPAVGLAFSIGAGAQTSNPP ATFSPDRINNKTREAHAQAIRELIH RDKNHPSVVMWSIANEPDTRPQGAR EYEAPLAEATRKLDPTRPITCVNVM ECDAHTDTISDLEDVLCLNRYYGWY VQSGDLETAEKVLEKELLAWQEKLH QPIIITEYGVDTLAGLHSMYTDMWS EEYQCAWLDMYHRVEDRVSAVVGEQ VWNFADFATSQSILRVGGNKKGIFT RDRKPKSAAFLLQKRWTGMNFGEKP QQGSKQGLCGR (Save5 chimera) 29 MLRPVETPTREIKKLDGLWAFSLDR ENCGIDQRWWESALQESRAIAVPGS ENDQFADADIRNYAGNVWYQREVFI PKGWAGQRIVLRFDAVTHYGKVWVN NQEVMEHQGGYTPFEADVTPYVIAG KSVRITVCVNNELNWQTIPPGMVIT DENGKKKQSYFHDFFNYAGIHRSVM LYTTPNTWVDDITVVTHVAQDCNHA SVDWQVVANGDVSVELRDADQQVVA TGQGTSGTLQVVNPHLWQPGEGYLY ELCVTAKSQTECDIYPLRVGIRSVA VKGEQFLINHKPFYFTGEGRHEDAD LRGKGEDNVLMVHDHALMDWIGANS YRTSHYPYAEEMLDWADEHGIVVID ETAAVGENLSLGIGFEAGNKPKELY SEEAVNGETQQAHLQAIKELIARDK NHPSVVMWSIANEPASNEDGAREYE APLPKLARQLDPTRPVTFANVGLAT YKADRIADLEDVLCLNRYFGWYTQT AELDEAEAALEEELRGWTEKYDKPI VMTEYGADTVAGLHSVMVTPWSEEF QVEMLDMYHRVFDRFEAMAGEQVWN FADFQTAVGVSRVDGNKKGVFTRDR KPKAAAHLLRKRWTNLHNGTAEGSK QGLCGR (Save9 chimera) 30 MLRPVETPTREIKKLDGLWAFSLDR ENCGIDQRWWESALQESRAIAVPGS ENDQFADADIRNYAGNVWYQREVFI PKGWAGQRIVLRFDAVTHYGKVWVN NQEVMEHQGGYTPFEADVTPYVIAG KSVRITVCVNNELNWQTIPPGMVIT DENGKKKQSYFHDFFNYAGIHRSVM LYTTPNTWVDDITVVTHVAQDCNHA SVDWQVVANGDVSVELRDADQQVVA TGQGTSGTLQVVNPHLWQPGEGYLY ELCVTAKSQTECDIYPLRVGIRSVA VKGEQFLINHKPFYFTGFGKHEDSA VRGKGYDPAYMVHDFQLMDWMGANS FRTSHYPYAEEVMEFADRHGIVVID ETAAVGFNLSLGIGFEAGNKPKELY SEEAVNGETQQAHLQAIKELIARDK NHPSVVMWSIANEPASQEVGAREYF APLVDLAHELDPSRPVCFANYGDAT YEVDRISDMFDVLCLNRYFGWYSQT GEVEEAEAALEKELLGWEGKYGKPI VITEYGADTMAGLHSVLALPWSEEF QVQLLDMYHRVFDRIDSVVGEHVWN FADFQTAVGIIRVDGNKKGVFTRER KPKAAAHTLKTRWSGMLGSKQGLCG R (Save10 chimera) 31 MLKPQQTTTRDLISLDGLWKFALAS DDNNTQPWTSQLKTSLECPVPASYN DIFADSKIHDHVGWVYYQRDVIVPK GWSEERYLVRCEAATHHGRIYVNGN LVADHVGGYTPFEADITDLVAAGEQ FRLTIAVDNELTYQTIPPGKVEILE ATGKKVQTYQHDFYNYAGLARSVWL YSVPQQHIQDITVRTDVQGTTGLID YNVVASTTQGTIQVAVIDEDGTTVA TSSGSNGTIHIPSVHLWQPGAAYLY QLHASIIDSSKKTIDTYKLATGIRT VKVQGTQFLINDKPFYFTGFGKHED TNIRGKGHDDAYMVHDFQLLHWMGA NSFRTSHYPYAEEVMEYADRQGIVV IDETPAVGLAFSIGSGVSSEDSPQT FTPEGINNNTREAHKQAIRELIARD KNHASVVMWSIANEPASNEDGAREY FAPLPKLARQLDPTRPVTFANVGLA TYKADRIADLFDVLCLNRYFGWYTQ TAELDEAEAALEEELRGWTEKYDKP IVMTEYGADTVAGLHSVMVTPWSEE FQVEMLDMYHRVFDRFEAMAGEQVW NFADFQTAVGVSRVDGNKKGVFTRD RKPKAAAHLLRKRWTNLHNGTAEGS KTFQ (L1 chimera) 32 MLKPQQTTTRDLISLDGLWKFALAS DDNNTQPWTSQLKTSLECPVPASYN DIFADSKIHDHVGWVYYQRDVIVPK GWSEERYLVRCEAATHHGRIYVNGN LVADHVGGYTPFEADITDLVAAGEQ FRLTIAVDNELTYQTIPPGKVEILE ATGKKVQTYQHDFYNYAGLARSVWL YSVPQQHIQDITVRTDVQGTTGLID YNVVASTTQGTIQVAVIDEDGTTVA TSSGSNGTIHIPSVHLWQPGAAYLY QLHASIIDSSKKTIDTYKLATGIRT VKVQGTQFLINDKPFYFTGEGRHED ADLRGKGEDNVLMVHDHALMDWIGA NSYRTSHYPYAEEMLDWADEHGIVV IDETAAVGFNLSLGIGFEAGNKPKE LYSEEAVNGETQQAHLQAIKELIAR DKNHPSVVMWSIANEPASNEDGARE YFAPLPKLARQLDPTRPVTFANVGL ATYKADRIADLFDVLCLNRYFGWYT QTAELDEAEAALEEELRGWTEKYDK PIVMTEYGADTVAGLHSVMVTPWSE EFQVEMLDMYHRVFDRFEAMAGEQV WNFADFQTAVGVSRVDGNKKGVETR DRKPKAAAHLLRKRWTNLHNGTAEG SKTFQ (L2 chimera) 33 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGFGKH EDTNIRGKGHDDAYMVHDFQLLHWM GANSFRTSHYPYAEEVMEYADRQGI VVIDETPAVGLAFSIGAGAQTSNPP ATFSPDRINNKTREAHAQAIRELIH RDKNHPSVVMWSIANEPASQEVGAR EYFAPLVDLAHELDPSRPVCFANYG DATYEVDRISDMEDVLCLNRYFGWY SQTGEVEEAEAALEKELLGWEGKYG KPIVITEYGADTMAGLHSVLALPWS EEFQVQLLDMYHRVFDRIDSVVGEH VWNFADFQTAVGIIRVDGNKKGVFT RERKPKAAAHTLKTRWSGMLGSDH (L3 chimera) 34 MLKPRQTPERDLISLDGLWKFALDS GDNATAAPWTGPLTTDLECPVPASY NDIFVDRQIRDHVGWVYYQREAIVP RAWSQQQYLVRVDAATHQGRIYIND NLVAEHRGGYTPFEADITGLVSAGD SFRLTIAVNNELTHETIPPGRIEVE EYTGKRVQVYQHDFFNYAGLARSVW LYSVPQQHIQDIKVVTHVKGSAGLI NYLVTVSNSTTGRVKIDVIDKDGTT VAEASGARGSVTIDSVKLWQPGEAY LYQFRASIVGLNDSVVDTYCVETGV RTVKVSGNRFLINDKPFYFTGFGKH EDSAVRGKGYDPAYMVHDFQLMDWM GANSFRTSHYPYAEEVMEFADRHGI VVIDETAAVGENLSLGIGFEAGNKP KELYSEEAVNGETQQAHLQAIKELI ARDKNHPSVVMWSIANEPASQEVGA REYFAPLVDLAHELDPSRPVCFANY GDATYEVDRISDMEDVLCLNRYFGW YSQTGEVEEAEAALEKELLGWEGKY GKPIVITEYGADTMAGLHSVLALPW SEEFQVQLLDMYHRVEDRIDSVVGE HVWNFADFQTAVGIIRVDGNKKGVF TRERKPKAAAHTLKTRWSGMLGSDH (L4 chimera) 35 MLRPVETPTREIKKLDGLWAFSLDR ENCGIDQRWWESALQESRAIAVPGS ENDQFADADIRNYAGNVWYQREVFI PKGWAGQRIVLRFDAVTHYGKVWVN NQEVMEHQGGYTPFEADVTPYVIAG KSVRITVCVNNELNWQTIPPGMVIT DENGKKKQSYFHDFFNYAGIHRSVM LYTTPNTWVDDITVVTHVAQDCNHA SVDWQVVANGDVSVELRDADQQVVA TGQGTSGTLQVVNPHLWQPGEGYLY ELCVTAKSQTECDIYPLRVGIRSVA VKGEQFLINHKPFYFTGFGKHEDTN IRGKGHDDAYMVHDFQLLHWMGANS FRTSHYPYAEEVMEYADRQGIVVID ETPAVGLAFSIGAGAQTSNPPATFS PDRINNKTREAHAQAIRELIHRDKN HPSVVMWSIANEPDTRPQGAREYEA PLAEATRKLDPTRPITCVNVMECDA HTDTISDLFDVLCLNRYYGWYVQSG DLETAEKVLEKELLAWQEKLHQPII ITEYGVDTLAGLHSMYTDMWSEEYQ CAWLDMYHRVEDRVSAVVGEQVWNF ADFATSQSILRVGGNKKGIFTRDRK PKSAAFLLQKRWTGMNFGEKPQQGS KQGLCGR (L5 chimera) 36 MLRPVETPTREIKKLDGLWAFSLDR ENCGIDQRWWESALQESRAIAVPGS ENDQFADADIRNYAGNVWYQREVFI PKGWAGQRIVLRFDAVTHYGKVWVN NQEVMEHQGGYTPFEADVTPYVIAG KSVRITVCVNNELNWQTIPPGMVIT DENGKKKQSYFHDFFNYAGIHRSVM LYTTPNTWVDDITVVTHVAQDCNHA SVDWQVVANGDVSVELRDADQQVVA TGQGTSGTLQVVNPHLWQPGEGYLY ELCVTAKSQTECDIYPLRVGIRSVA VKGEQFLINHKPFYFTGEGRHEDAD LRGKGEDNVLMVHDHALMDWIGANS YRTSHYPYAEEMLDWADEHGIVVID ETPAVGLAFSIGSGVSSEDSPQTFT PEGINNNTREAHKQAIRELIARDKN HASVVMWSIANEPDTRPQGAREYEA PLAEATRKLDPTRPITCVNVMECDA HTDTISDLEDVLCLNRYYGWYVQSG DLETAEKVLEKELLAWQEKLHQPII ITEYGVDTLAGLHSMYTDMWSEEYQ CAWLDMYHRVEDRVSAVVGEQVWNF ADFATSQSILRVGGNKKGIFTRDRK PKSAAFLLQKRWTGMNFGEKPQQGS KQGLCGR (L6 chimera) 37 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGG KANMMSGMMGGMGAGASDKPQNNPN EDFFNYAGLNRPVKITVTNKEYIHD IDILSDVNGSDGIVNYEVHTTGENK VYIKINDEEGKEVASCEGKSGKIVI KDAKLWNPKAAYLYKFIACIKNGDE LIDEYYLDEGIRTVKVEGTKFLING KPFYFTGFGKHEDSEIAGRGYNPPV IKRDFELIKWVGANSFRTSHYPYSE EIMQAADREGIVIIDEVAAVGMFDV GSVLNPSASKTDYFSLDEVHSKTKE VHKKAVEELIKRDKNHPSVVMWSLE NEPDTSKDEAVPYFEDIENFAKSQD KQNLPKTFAAIQASSPGKCKCMHLC DVITLNRYYGWYFLGGYEIDMSEEK FREEMNLYSNMNKPVMFTEYGADTY AGVHKLPSVMWSEEYQCEYYEMNEK VEDSYDFIVGEQLWNFADFQTTEGI FRVDGNKKGIFTRNRQPKAVAHLIR SRWNKLPLDYKSKK (BpChimera1 chimera) 38 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDQFGGGANEGGER IGTEDKEHGSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpChimera2 chimera) 39 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGG KANMMSGMMGGMGAGASDKPQNNPN EDFFNYAGLNRPVKITVTNKEYIHD IDILSDVNGSDGIVNYEVHTTGENK VYIKINDEEGKEVASCEGKSGKIVI KDAKLWNPKAAYLYKFIACIKNGDE LIDEYYLDEGIRTVKVEGTKFLING KPFYFTGFGKHEDSEIAGRGYNPPV IKRDFELIKWVGANSFRTSHYPYSE EIMQAADREGIVIIDEVAAVGMFDQ FGGGANEGGERIGTEDKEHGSKTKE VHKKAVEELIKRDKNHPSVVMWSLE NEPDTSKDEAVPYFEDIENFAKSQD KQNLPKTFAAIQASSPGKCKCMHLC DVITLNRYYGWYFLGGYEIDMSEEK FREEMNLYSNMNKPVMFTEYGADTY AGVHKLPSVMWSEEYQCEYYEMNEK VEDSYDFIVGEQLWNFADFQTTEGI FRVDGNKKGIFTRNRQPKAVAHLIR SRWNKLPLDYKSKK (BpChimera3 chimera) 40 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGGGGANEGGER DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpChimera4 chimera) 41 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGG KANMMSGMMGGMGAGASDKPQNNPN EDFFNYAGLNRPVKITVTNKEYIHD IDILSDVNGSDGIVNYEVHTTGENK VYIKINDEEGKEVASCEGKSGKIVI KDAKLWNPKAAYLYKFIACIKNGDE LIDEYYLDEGIRTVKVEGTKFLING KPFYFTGFGKHEDSEIAGRGYNPPV IKRDFELIKWVGANSFRTSHYPYSE EIMQAADREGIVIIDEVAAVGMFDV GGGGANEGGERDYFSLDEVHSKTKE VHKKAVEELIKRDKNHPSVVMWSLE NEPDTSKDEAVPYFEDIENFAKSQD KQNLPKTFAAIQASSPGKCKCMHLC DVITLNRYYGWYFLGGYEIDMSEEK FREEMNLYSNMNKPVMFTEYGADTY AGVHKLPSVMWSEEYQCEYYEMNEK VEDSYDFIVGEQLWNFADFQTTEGI FRVDGNKKGIFTRNRQPKAVAHLIR SRWNKLPLDYKSKK (BpChimeraS chimera) 42 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGCGHTET KPSGKKYIKPSEDFFNYCGITRPVK IYTTPETYINDITVTADIDETKEEP SAVLNYNVEIKGKDYNNITCKVELF DEEGTKLSETEGSEGTFEISNVRLW QPLNAYLYKIKVTAGQDVYTLPYGV RSVRVDGTKFLINEKPFYFKGYGKH EDTFPNGRGINLPMNTKDISIMKWQ HANSFRTSHYPYSEEMMRLCDEEGI VVIDETTAVGVNLQFGGGANEGGER IGTEDKEHGVQTQEHHKDVIRDLIS RDKNHACVVMWSIANEPDSAAEGAY DYFKPLYDLARELDPQKRPCTLVSV QGTTADTDCSSQLSDVICLNRYYGW YEGGPDLEVSEIGLRKELSDWGKLG KPVMFTEYGADTVSGLHDTTSVMYT EEYQVEYYEMNNKVFDEFDFVVGEQ AWNFADFATSQSLLRVQGNKKGLFT RDRKPKMVAHYFRNRWSTIPEFGYK TK (EeChimeral chimera) 43 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNFDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLVGS VLNPSASKTDYFSLDEVHVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeChimera2 chimera) 44 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGCGHTET KPSGKKYIKPSEDFFNYCGITRPVK IYTTPETYINDITVTADIDFTKEEP SAVLNYNVEIKGKDYNNITCKVELF DEEGTKLSETEGSEGTFEISNVRLW QPLNAYLYKIKVTAGQDVYTLPYGV RSVRVDGTKFLINEKPFYFKGYGKH EDTFPNGRGINLPMNTKDISIMKWQ HANSFRTSHYPYSEEMMRLCDEEGI VVIDETTAVGVNLVGSVLNPSASKT DYFSLDEVHVQTQEHHKDVIRDLIS RDKNHACVVMWSIANEPDSAAEGAY DYFKPLYDLARELDPQKRPCTLVSV QGTTADTDCSSQLSDVICLNRYYGW YEGGPDLEVSEIGLRKELSDWGKLG KPVMFTEYGADTVSGLHDTTSVMYT EEYQVEYYEMNNKVFDEFDFVVGEQ AWNFADFATSQSLLRVQGNKKGLFT RDRKPKMVAHYFRNRWSTIPEFGYK TK (EeChimera3 chimera) 45 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNFDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFS VLNPSASKTIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDEVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeChimera4 chimera) 46 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGCGHTET KPSGKKYIKPSEDFFNYCGITRPVK IYTTPETYINDITVTADIDETKEEP SAVLNYNVEIKGKDYNNITCKVELF DEEGTKLSETEGSEGTFEISNVRLW QPLNAYLYKIKVTAGQDVYTLPYGV RSVRVDGTKFLINEKPFYFKGYGKH EDTFPNGRGINLPMNTKDISIMKWQ HANSFRTSHYPYSEEMMRLCDEEGI VVIDETTAVGVNLQFSVLNPSASKT IGTEDKEHGVQTQEHHKDVIRDLIS RDKNHACVVMWSIANEPDSAAEGAY DYFKPLYDLARELDPQKRPCTLVSV QGTTADTDCSSQLSDVICLNRYYGW YEGGPDLEVSEIGLRKELSDWGKLG KPVMFTEYGADTVSGLHDTTSVMYT EEYQVEYYEMNNKVFDEFDFVVGEQ AWNFADFATSQSLLRVQGNKKGLFT RDRKPKMVAHYFRNRWSTIPEFGYK TK (EeChimera5 chimera) 47 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYATGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294A) 48 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYITGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294I) 49 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYVTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294V) 50 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYYTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294Y) 51 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYLTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294L) 52 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYWTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294W) 53 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYWKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDEVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeF303W) 54 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYSKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDEVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeF303S) 55 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFAGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295A) 56 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFCGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295C) 57 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFFGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295F) 58 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFIGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295I) 59 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFKGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295K) 60 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFSGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295S) 61 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFVGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295V) 62 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFAGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeK304A) 63 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFVGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeK304V) 64 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAF QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpI450F) 65 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAK QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK  (Bp1450K) 66 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAL QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpI450L) 67 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAM QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpI450M) 68 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAQ QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpI450Q) 69 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAD QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK  (BpI450D) 70 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAV QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpI450V) 71 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSFQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeV459F) 72 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSLQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeV459L) 73 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSWQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDEVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeV459W) 74 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSCQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeV459C) 75 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSGQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDEVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeV459G) 76 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSEQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDEVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeV459E) 77 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI DASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451D) 78 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI EASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451E) 79 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI GASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451G) 80 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI SASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451S) 81 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI VASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451V) 82 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI KASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451K) 83 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QDSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpA452D) 84 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QKSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpA452K) 85 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QNSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpA452N) 86 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QGSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpA452G) 87 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QESSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpA452E) 88 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QQSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpA452Q) 89 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQATTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeG461A) 90 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQHTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeG461H) 91 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQNTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeG461N) 92 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQSTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDEVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeG461S) 93 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEEIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpG563E) 94 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEAIERVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpG563A) 95 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEDIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpG563D) 96 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEYIERVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpG563Y) 97 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQGLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeS571G) 98 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVED EFDEVVGEQAWNFADFATSQNLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeS571N) 99 MLKPQQTTTRDLISLDGLWKFALAS DDNNTQPWTSQLKTSLECPVPASYN DIFADSKIHDHVGWVYYQRDVIVPK GWSEERYLVRCEAATHHGRIYVNGN LVADHVGGYTPFEADITDLVAAGEQ FRLTIAVDNELTYQTIPPGKVEILE ATGKKVQTYQHDFYNYAGLARSVWL YSVPQQHIQDITVRTDVQGTTGLID YNVVASTTQGTIQVAVIDEDGTTVA TSSGSNGTIHIPSVHLWQPGAAYLY QLHASIIDSSKKTIDTYKLATGIRT VKVQGTQFLINDKPFYFTGFGKHED SAVRGKGYDPAYMVHDFQLMDWMGA NSFRTSHYPYAEEVMEFADRHGIVV IDETPAVGLAFSIGSGVSSEDSPQT FTPEGINNNTREAHKQAIRELIARD KNHASVVMWSIANEPASQEVGAREY FAPLVDLAHELDPSRPVCFANYGDA TYEVDRISDMFDVLCLNRYFGWYSQ TGEVEEAEAALEKELLGWEGKYGKP IVITEYGADTMAGLHSVLALPWSEE FQVQLLDMYHRVEDRIDSVVGEHVW NFADFQTAVVIIRVDGNKKGVETRE RKPKAAAHTLKTRWSGMLGSDH (Rxn3G560V) 100 MLKPQQTTTRDLISLDGLWKFALAS DDNNTQPWTSQLKTSLECPVPASYN DIFADSKIHDHVGWVYYQRDVIVPK GWSEERYLVRCEAATHHGRIYVNGN LVADHVGGYTPFEADITDLVAAGEQ FRLTIAVDNELTYQTIPPGKVEILE ATGKKVQTYQHDFYNYAGLARSVWL YSVPQQHIQDITVRTDVQGTTGLID YNVVASTTQGTIQVAVIDEDGTTVA TSSGSNGTIHIPSVHLWQPGAAYLY QLHASIIDSSKKTIDTYKLATGIRT VKVQGTQFLINDKPFYFTGFGKHED SAVRGKGYDPAYMVHDFQLMDWMGA NSFRTSHYPYAEEVMEFADRHGIVV IDETPAVGLAFSIGSGVSSEDSPQT FTPEGINNNTREAHKQAIRELIARD KNHASVVMWSIANEPASQEVGAREY EAPLVDLAHELDPSRPVCFANYGDA TYEVDRISDMFDVLCLNRYFGWYSQ TGEVEEAEAALEKELLGWEGKYGKP IVITEYGADTMAGLHSVLALPWSEE FQVQLLDMYHRVEDRIDSVVGEHVW NFADFQTAVEIIRVDGNKKGVETRE RKPKAAAHTLKTRWSGMLGSDH (Rxn3G560E) 101 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYYCGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294Y/T295C) 102 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYYIGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294Y/T295I) 103 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYYVGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294Y/T295V) 104 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYYFGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294Y/T295F) 105 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYYMGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294Y/T295M) 106 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYYKGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpF294Y/T295K) 107 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFVGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAL QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295V/I450L) 108 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFVGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAM QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295V/I450M) 109 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFVGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAY QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295V/I450Y) 110 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFVGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAV QASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpT295V/I450V) 111 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAM DASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpI450M/Q451D) 112 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAQ DASSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpI450Q/Q451D) 113 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI DESSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451D/A452E) 114 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI DGSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451D/A452G) 115 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI DQSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451D/A452Q) 116 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI DSSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451D/A452S) 117 MVNSMLYPRESRTRRVVDISGMWEF KIDINNEGRNSGYANGLKDTTFIPV PSSENDLFTDKNIREHAGDVWYETS FYLPLEWKDKDVNVREGCATHEATV YINGKEVCTHVGGFMPFNAPVNEAG IFGEKNKLVVVVNNELSNTTIPCGH TETKPSGKKYIKPSEDFFNYAGLNR PVKITVTNKEYIHDIDILSDVNGSD GIVNYEVHTTGENKVYIKINDEEGK EVASCEGKSGKIVIKDAKLWNPKAA YLYKFIACIKNGDELIDEYYLDEGI RTVKVEGTKFLINGKPFYFTGFGKH EDSEIAGRGYNPPVIKRDFELIKWV GANSFRTSHYPYSEEIMQAADREGI VIIDEVAAVGMFDVGSVLNPSASKT DYFSLDEVHSKTKEVHKKAVEELIK RDKNHPSVVMWSLENEPDTSKDEAV PYFEDIENFAKSQDKQNLPKTFAAI DRSSPGKCKCMHLCDVITLNRYYGW YFLGGYEIDMSEEKFREEMNLYSNM NKPVMFTEYGADTYAGVHKLPSVMW SEEYQCEYYEMNEKVEDSYDFIVGE QLWNFADFQTTEGIFRVDGNKKGIF TRNRQPKAVAHLIRSRWNKLPLDYK SKK (BpQ451D/A452R) 118 MLYPVLTCSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNICVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeQ8C/S73C) 119 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGPDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSQSLLRV QGNKKGLFTRDRCPKMVAHYFRNRW STIPEFGYKTK (EeK588C) 120 MLYPVLTQSRLLSDLSGVWDFKLDN GKGFEEKWYEKPLKDADTMPVPASY NDLKEGTDFRDHYGWVFYQRNISVP EYVKSQRIVLRCAAVTHYAMIYLNG KLICEHKGGFLPFEVELNDDLQDGD NLLTIAVNNVIDYTTLPVGGKANMM SGMMGGMGAGASDKPQNNPNEDFFN YCGITRPVKIYTTPETYINDITVTA DIDETKEEPSAVLNYNVEIKGKDYN NITCKVELFDEEGTKLSETEGSEGT FEISNVRLWQPLNAYLYKIKVTAGQ DVYTLPYGVRSVRVDGTKFLINEKP FYFKGYGKHEDTFPNGRGINLPMNT KDISIMKWQHANSFRTSHYPYSEEM MRLCDEEGIVVIDETTAVGVNLQFG GGANEGGERIGTEDKEHGVQTQEHH KDVIRDLISRDKNHACVVMWSIANE PDSAAEGAYDYFKPLYDLARELDPQ KRPCTLVSVQGTTADTDCSSQLSDV ICLNRYYGWYEGGCDLEVSEIGLRK ELSDWGKLGKPVMFTEYGADTVSGL HDTTSVMYTEEYQVEYYEMNNKVFD EFDFVVGEQAWNFADFATSCSLLRV QGNKKGLFTRDRKPKMVAHYFRNRW STIPEFGYKTK (EeP489C/Q570C) 

What is claimed is:
 1. A composition comprising a blend of Eubacterium eligens beta-glucuronidase (EeGUS) enzyme and a chimeric beta-glucuronidase enzyme Rxn3, wherein EeGUS comprises the amino acid sequence shown in SEQ ID NO: 10 and Rxn3 comprises the amino acid sequence shown in SEQ II) NO:
 19. 2. A formulation comprising the composition of claim 1, wherein the formulation comprises the blend of EeGUS and Rxn 3 and at least one excipient.
 3. The formulation of claim 2, which is an aqueous formulation.
 4. The formulation of claim 2, which is a lyophilized formulation.
 5. The formulation of claim 2, wherein the at least one excipient is selected from the group consisting of water, salts, buffers, sugars and amino acids.
 6. The formulation of claim 2, which is free of polymers and detergents.
 7. The formulation of claim 2, wherein each enzyme in the blend is present at a concentration of at least 0.1 mg/mL.
 8. A packaged formulation comprising the formulation of claim 2 and a container.
 9. The composition of claim 1, wherein the blend comprises 70%-90% EeGUS and 10%-30% Rxn3.
 10. The composition of claim 1, wherein the blend comprises 70%-80% EeGUS and 20%-30% Rxn3.
 11. The composition of claim 1, wherein the blend comprises 70% EeGUS and 30% Rxn3.
 12. The composition of claim 1, wherein the blend comprises 80% EeGUS and 20% Rxn3. 